Developmental and School-Related Risk Factors in Auditory Processing Disorder: A Pilot Study in Polish Children

Abstract

The paper presents the issue of acquired and secondary auditory processing disorder (APD) in children and adolescents in the Polish population. The authors analyzed a group of individuals with APD and younger children who were at risk based on a detailed interview with parents. A comparison of developmental factors showed several similarities between the risk and diagnosed APD groups, including abnormal muscle tone (64.29% vs. 33.33%), ear diseases (42.86% vs. 57.58%), and complicated delivery (32.14% vs. 39.39%). In terms of school factors, the most significant difficulties were associated with poor concentration (78.57% vs. 54.55%), irregularities in mastering phonology related to writing (67.86% vs. 75.76%), and reading (64.29% vs. 78.79%), as well as problems with understanding speech-in-noise perception (60.71% vs. 57.58%). A comparison of children at risk of APD and those with a confirmed diagnosis revealed multiple similarities. The results were visualized using Pareto charts to highlight the most influential factors. The results indicate the need to disseminate screening that could show the APD risk group. Therefore, the diagnostic process could be performed more quickly in such individuals. Based on recurring developmental factors, the Risk Assessment Questionnaire (RAQ) was developed as a non-clinical screening tool to identify children potentially at risk of APD. The RAQ demonstrated a moderate discriminative potential (AUC = 0.68; sensitivity = 75%; specificity = 68%) and may support early referral for diagnostic evaluation. The results highlight the value of systematic screening to accelerate diagnosis and intervention, especially in populations where access to formal assessment is limited.

1. Introduction

Auditory processing disorder (APD), also known as central auditory processing disorder (CAPD), is associated with abnormalities at higher auditory functions, as discussed in studies from the fields of medicine, speech therapy (speech and language therapy for hearing), and pedagogy [1,2,3,4,5]. Due to the nature of auditory processing and the role of the auditory cortex, it is natural to link its dysfunction to other complex processes, including cognitive functions, as well as the occurrence of auditory processing disorders in children with learning disabilities [6,7,8]. However, Maggu [9] emphasized a significantly weaker relationship between cognitive abilities and auditory processing in adult patients. Both recent and earlier research on APD [1,9,10,11] has highlighted the need to distinguish auditory processing disorders from language impairments of different etiology. This distinction is often challenging due to the necessity of adapting test procedures or incorporating non-linguistic assessments into the diagnostic process [12,13].

In APD, children and adolescents experience a set of auditory processing difficulties that significantly impact their academic performance and daily communication [4,5,6,14,15]. The auditory cortex processes sounds, primarily speech, while minimizing interference with concurrent cognitive and motor tasks. Therefore, efficient auditory processing requires attention, automation, and appropriate cortical synchronization. If this process is impaired, children may present with delayed language acquisition, difficulties in understanding speech in noisy environments, and learning disabilities [16].

APD is not caused by peripheral hearing loss but rather by dysfunction in the central auditory nervous system (CANS). As a result, children with APD often pass standard audiometric tests but continue to struggle with auditory perception and comprehension. These difficulties frequently translate into poor academic achievement, attention deficits, and problems in social interactions [3,4,17].

Research on the etiology of APD identifies three main causes, i.e., developmental, acquired, and secondary APD [3,18]. Developmental APD is associated with neurological immaturity, delayed myelination, and deficits in neuronal connectivity, without a clear external cause. In contrast, acquired APD results from identifiable risk factors such as perinatal complications, prematurity, brain injuries, infections, or exposure to ototoxic agents [5,19]. Additionally, increasing exposure to digitally processed sounds from electronic devices in early childhood has been linked to auditory deprivation and delayed auditory system maturation [20,21].

The symptoms of APD often overlap with other developmental disorders, such as specific learning disorders (SLDs), attention deficit hyperactivity disorder (ADHD), and language impairment. Therefore, differential diagnosis is essential and should include standard audiological testing, speech-language assessments, and neuropsychological evaluation. In addition, it should also cover objective tests, including electrophysiological tests, such as frequency following response [13,22,23,24,25,26,27,28,29]. In practice, however, conducting differential diagnosis significantly prolongs the diagnostic process. Although these mechanisms are well described in previous research, they provide the necessary clinical context for the RAQ tool developed in this study.

The aim of this study was to analyze the relationship between developmental and school-related risk factors in Polish children diagnosed with APD and those at risk of developing the disorder. By identifying common characteristics in both groups, this study aimed to improve early identification strategies and highlight the importance of preventive interventions. While APD has been extensively studied internationally, its combined developmental and school-related risk factors remain underexplored in the Polish population. This study addresses this gap by comparing children at risk of APD with those formally diagnosed. We hypothesized that both groups would exhibit similar developmental risk factors, with school-related difficulties more pronounced in the diagnosed group. Based on recurring developmental patterns observed during the study, we developed the Risk Assessment Questionnaire (RAQ) as a preliminary, non-clinical screening tool to support early identification and referral for APD assessment.

2. Materials and Methods

2.1. Study Design and Participants

This study aimed to identify risk factors in two groups: (1) children diagnosed with APD and (2) children at risk of APD. Next, we analyzed the relationship between these factors and both groups.

The study was conducted as a cross-sectional observational study and included 60 children (aged 6–12 years). The participants were divided into two groups:

• APD-risk group (n = 28): children who presented with symptoms suggestive of APD but had not received a formal diagnosis.
• APD-diagnosed group (n = 32): children with a confirmed APD diagnosis based on audiological and speech-language assessments.

Table 1. The characteristics of the study groups.

Group	Number of Children (F/M)	Mean Age (F/M)
APD-risk	6/22	8 ± 1 years/7 ± 1 years
APD-diagnosis	10/22	10 ± 2 years/10 ± 2 years

shows the characteristics of the study groups, including the number of children and their mean age in the APD-risk and APD-diagnosed groups. The predominance of boys in both groups reflects the fact that APD symptoms are more frequently reported in males than in females, as supported by previous studies [2,3]. Moreover, the clinical recruitment setting may have further influenced the gender distribution in the sample.

All participants were recruited from speech therapy and audiology clinics in the Silesian region, Poland, with parental consent obtained prior to participation. This study adhered to the principles of the Declaration of Helsinki. The study was based on anonymized, retrospective clinical interview data and did not involve experimental intervention. The Committee on Ethics of Research Involving Human Participants at the Silesian University of Technology issued a positive opinion, Resolution No. 5/2025, dated 26 September 2025, confirming that the study meets ethical standards.

The participants were eligible for inclusion if they had normal peripheral hearing (confirmed by pure-tone audiometry), were aged 6–12 years, and had no history of intellectual disability or neurological disorders. Children in the APD-risk group were included based on clinical suspicion of APD, whereas children in the APD-diagnosed group had a confirmed diagnosis.

The exclusion criteria were as follows: sensorineural or conductive hearing loss, neurodevelopmental disorders (e.g., autism, ADHD, dyslexia) without coexisting APD, and a history of severe neurological diseases such as epilepsy, cerebral palsy, or traumatic brain injury.

Children were referred for diagnosis due to difficulties in acquiring school skills based on auditory-related activities. According to the standardized Polish diagnostic procedures, Wechsler intelligence tests were conducted to assess cognitive abilities [3]. Subsequently, physiological hearing tests were performed, including impedance audiometry and pure-tone audiometry, confirming normal hearing thresholds. Additionally, speech-language and pedagogical assessments were conducted.

Higher auditory functions were evaluated using standardized tests, including the Duration Pattern Test (DPT), the Frequency Pattern Test (FPT), and the Dichotic Digit Test (DDT) [3,30,31]. The final diagnosis of APD was established by a specialist in audiology and phoniatrics based on the findings.

The diagnosis and management of APD vary across countries, with differences in diagnostic criteria and the availability of standardized tests. In Poland, there is an increasing need for early screening tools that can help identify children at risk of APD before significant school-related difficulties occur. This tool aims to facilitate early intervention and support educational planning for children at risk.

2.2. Data Collection

The structured parental interviews were conducted by the authors as part of routine clinical procedures. The thematic scope of the interview was informed by existing literature on developmental and medical risk factors [3,32]. The interview included questions about prenatal and perinatal history, such as pregnancy complications and birth conditions, as well as medical history, including recurrent ear infections, respiratory diseases, and allergies. Additionally, parents provided information on developmental milestones, including speech and language development and motor skills. The interview also addressed school performance, focusing on reading, writing, concentration, and the ability to understand speech in noise. The interviews were conducted by two specialists (a neurologist and a speech-language therapist specializing in hearing disorders).

2.3. Developmental Factors

Developmental risk factors are given in

Table 2. Prevalence of developmental risk factors in APD-risk and APD-diagnosed groups.

Developmental Factor	Description
High-risk pregnancy (HRPr)	Pregnancy complications potentially leading to brain microdamage (e.g., maternal infections, hypoxia)
High-risk childbirth (HRCh)	Birth-related complications (e.g., premature birth, low birth weight, hypoxia)
Recurrent upper respiratory tract infections (IURTr)	Frequent infections affecting middle ear function and auditory pathways
Allergy or asthma (AAst)	Respiratory conditions impacting auditory processing
Middle and inner ear diseases (EaDi)	Chronic otitis media, Eustachian tube dysfunction, and other auditory disorders
Abnormal muscle tone (AbMT)	Orofacial muscle dysfunction affecting speech and hearing
Hypertrophy of adenoids (HyAd)	Enlarged adenoids causing Eustachian tube dysfunction and auditory processing difficulties
Auditory deprivation (AuDe)	Overexposure to digitally processed sounds (e.g., excessive use of electronic devices)

based on the clinical expertise of the study team and previous research [33,34,35,36,37,38,39,40]. These indicators are consistent with functional impairment observed in children with auditory processing issues and were used to create the RAQ.

2.4. School-Related Factors

School difficulties were identified based on clinical APD profiles [29,32,41] and expert assessment. The analyzed factors are included in

Table 3. School-related difficulties in children with APD-risk and APD-diagnosed groups.

School Factor	Description
Articulation difficulties (ArD)	Pronunciation errors persisting beyond the expected developmental period
Difficulties in coherence of statements (DCoSt)	Problems with sentence structure, grammar, and fluency in speech
Phonological abnormalities in reading (PAR)	Difficulty in decoding words, sound blending, and phonetic awareness
Phonological abnormalities in writing (PAW)	Problems with spelling, letter recognition, and writing automation
Difficulties in concentration (DCo)	Inability to focus on auditory tasks for extended periods
Sound source localization difficulties (IrSL)	Problems identifying the direction and source of sounds
Hyperacusis (Hyp)	Hypersensitivity to environmental sounds, causing discomfort and distraction
Auditory memory deficits (DAMe)	Problems remembering and processing verbal information
Speech comprehension in noise (IUSN)	Difficulty understanding speech in noisy environments

.

2.5. Risk Assessment Questionnaire (RAQ): Construction and Preliminary Validation

The RAQ consists of 9 binary (yes/no) items, each assigned a weight of 1 or 2 points based on their frequency in the clinical sample and their relevance as reported in the literature (e.g., abnormal muscle tone, middle ear disease, recurrent infections) [33,34,36,37,39,40,42]. The maximum total score is 14 points. One item concerning habitual mouth breathing was included based on clinical observations, due to its association with other factors. A complete mapping of RAQ items to developmental risk categories is given in Appendix B.

To evaluate its initial utility, the RAQ was administered to parents of the original 60 participants after structured interviews (APD-risk and APD-diagnosed groups). A reference group of 30 children without diagnosed APD confirmed by the Frequency Pattern Test (FPT), the Duration Pattern Test (DPT), and the Dichotic Digit Test (DDT) was later added exclusively for comparative analysis. The aim was to assess internal consistency and the tool’s screening potential. Performance metrics, including the ROC curve, sensitivity, specificity, and the Youden Index, are given in Section 3.4.

The Risk Assessment Questionnaire (RAQ) was not part of the original study design. It emerged as an exploratory outcome from the statistical analysis of developmental risk indicators in children diagnosed with APD and those identified as at risk. The repeated presence of specific developmental factors suggested the potential for a simple screening checklist to support early identification of such cases.

2.6. Statistical Analysis

Statistical analysis identified significant relationships between children at risk and those diagnosed with APD, considering developmental and school factors. Statistical significance levels are marked as * p < 0.05, ** p < 0.01 and *** p < 0.001. We verified the normality of data distribution using the Shapiro–Wilk test, which showed a deviation from the normal distribution. Then, we employed a Pareto chart to identify key development and school factors. Kendall rank correlation coefficient and the Chi-square test (with Cramér’s V as the effect size measure) were used to assess and confirm the relationship between factors in each group. The analyses were performed in the JASP computing environment [43]. Additionally, RAQ scores were analyzed using the Kruskal–Wallis test and post hoc Mann–Whitney tests to examine differences across diagnostic groups. Effect sizes were reported as epsilon-squared (ε²) for Kruskal–Wallis tests and r for Mann–Whitney tests.

3. Results

The statistical analysis showed several similarities between the APD-risk group and APD-diagnosed group in terms of developmental and school-related factors. The results are presented in tabular and graphical forms using Pareto charts, which illustrate the most significant contributing factors.

3.1. Developmental Factors

The frequency of developmental factors in both groups is shown in

Table 4. Frequency of developmental factors in APD-risk group and APD-diagnosed group.

	APD-Risk Group			APD-Diagnosed Group
Developmental Factor	No.	Pct.	Pct. of Population	No.	Pct.	Pct. of Population
Abnormal muscle tone (AbMT)	18	28.57%	64.29%	11	15.94%	33.33%
Middle and inner ear diseases (EaDi)	12	19.05%	42.86%	19	27.54%	57.58%
Recurrent infections of the upper respiratory tract (IURTr)	10	15.87%	35.71%	6	8.70%	18.18%
High-risk childbirth (HRCh)	9	14.29%	32.14%	13	18.84%	39.39%
High-risk pregnancy (HRPr)	4	6.35%	14.29%	3	4.35%	9.09%
Auditory deprivation (AuDe)	4	6.35%	14.29%	4	5.80%	12.12%
Hypertrophy of adenoids (HyAd)	3	4.76%	10.71%	5	7.25%	15.15%
Allergy or asthma (AAst)	3	4.76%	10.71%	8	11.59%	24.24%

. The most commonly reported risk factors among APD-risk group children were abnormal muscle tone (64.29%), ear diseases (42.86%), recurrent upper respiratory tract infections (35.71%), and high-risk childbirth (32.14%). In the APD-diagnosed group, the most frequent factors included ear diseases (57.58%), high-risk childbirth (39.39%), abnormal muscle tone (33.33%), and allergy or asthma (24.24%).

The differences in developmental factors between the two groups suggest that while many children at risk of APD share early-life vulnerabilities with those formally diagnosed, these factors appear to exert a greater impact in the latter group. The high prevalence of recurrent ear infections (57.58%) and perinatal complications (39.39%) in the diagnosed group supports the existing evidence that early auditory disruptions may interfere with central auditory pathway maturation. This highlights the importance of proactive monitoring in at-risk children, even before a formal diagnosis.

The Pareto charts in Figure 1 illustrate the cumulative impact of these developmental factors in both groups. In the APD-risk group, the steepest slopes were observed for abnormal muscle tone, ear diseases, and upper respiratory tract infections. In the APD-diagnosed group, the dominant risk factors included ear diseases, high-risk childbirth, and abnormal muscle tone.

3.2. School-Related Factors

The occurrence of school difficulties in both groups is summarized in

Table 5. Frequency of school-related difficulties in APD-risk and APD-diagnosed groups.

	APD-Risk Group			APD-Diagnosed Group
School Factors	Number	Pct.	Pct. of Population	Number	Pct.	Pct. of Population
Concentration (DCo)	22	18.64%	78.57%	18	13.85%	54.55%
Phonological abnormalities in writing (PAW)	19	16.10%	67.86%	25	19.23%	75.76%
Phonological abnormalities in reading (PAR)	18	15.25%	64.29%	26	20.00%	78.79%
Understanding of speech in the presence of noise (IUSN)	17	14.41%	60.71%	19	14.62%	57.58%
Articulation difficulties (ArD)	16	13.56%	57.14%	13	10.00%	39.39%
Coherence of statements (DCoSt)	10	8.47%	35.71%	13	10.00%	39.39%
Auditory memory (DAMe)	6	5.08%	21.43%	6	4.62%	18.18%
Hyperacusis (Hyp)	5	4.24%	17.86%	1	0.77%	3.03%
Sound source localization (IrSL)	5	4.24%	17.86%	9	6.92%	27.27%

. Among APD-risk group children, the most significant school-related problems were poor concentration (78.57%), phonological abnormalities in writing (67.86%) and reading (64.29%), and difficulties understanding speech in noise (60.71%). Similarly, in the APD-diagnosed group, the most frequent school-related issues included phonological abnormalities in reading (78.79%) and writing (75.76%), understanding speech in noise (57.58%), and concentration difficulties (54.55%).

The Pareto charts in Figure 2 demonstrate that phonological processing difficulties (reading and writing) were the most prevalent school-related issues in both groups.

3.3. Association Between Developmental and School Difficulties

The Kendall’s tau-b correlation coefficient showed (

Table 6. The relationship between developmental and school factors based on Kendall’s rank correlation coefficient.

			APD-Risk Group		APD-Diagnosed Group
			Kendall’s tau-b	p	Kendall’s tau-b	p
Developmental factors	-	School factors	0.043	0.677	0.417 ***	<0.001

*** p < 0.001.

) no significant relationship between developmental and school-related factors in the APD-risk group (tau-b = 0.043, p = 0.677). However, a statistically significant correlation was found in the APD-diagnosed group (tau-b = 0.417, p < 0.001).

The Chi-square test did not show significant differences between the two groups (Χ² = 7.656; df = 7; p = 0.364; Cramér’s V = 0.14), suggesting that developmental risk factors were similarly distributed. These findings indicate that while developmental factors were present in both groups, their impact on school-related difficulties was more pronounced in children with a confirmed APD diagnosis. The multidimensional relationships between developmental and school-related factors are visualized in Appendix C, where heatmaps show their co-occurrence patterns across the APD-risk and APD-diagnosed groups.

3.4. Risk Assessment Questionnaire (RAQ)

RAQ scores significantly differed across the three groups (APD-risk, APD-diagnosed, reference) (Kruskal–Wallis test, p = 0.0019, ε² = 0.117). Post hoc Mann–Whitney analysis revealed significant differences between the reference group and both APD groups, but not between the APD-risk and APD-diagnosed groups (r for APD-diagnosed vs. APD-risk: 0.131; r for APD-diagnosed vs. reference: 0.319; r for APD-risk vs. reference: 0.435). Receiver operating characteristic (ROC) analysis yielded an AUC of 0.68, with a sensitivity of 75% and specificity of 68% at an optimal cut-off score of 4 points (Youden Index). These findings are illustrated in Figure 3, which presents (a) the ROC curve demonstrating the discriminative ability of the RAQ, and (b) the distribution of RAQ scores across diagnostic groups, with detailed descriptive statistics provided in

Table 7. Descriptive statistics for total RAQ scores in children diagnosed with APD, at risk of APD, and in the reference group.

Group	Mean RAQ Score	Median	SD	Min	Max
APD-risk	5.29	5.00	2.57	0	11.00
APD-diagnosed	4.53	5.00	2.31	0	11.00
Reference group	3.10	3.00	2.29	0	9.00

.

While these values suggest moderate discriminative ability, the RAQ is not intended for diagnostic use. Instead, it is a preliminary screening tool to support early referral and observation. Given that development and evaluation were performed on overlapping samples, the results should be interpreted with caution and validated in future research using independent cohorts.

4. Discussion

The study demonstrated that both the APD-risk and APD-diagnosed groups shared several developmental risk factors, including abnormal muscle tone, recurrent ear infections, and high-risk childbirth. These findings are consistent with previous studies that have highlighted the negative impact of neuromuscular and middle-ear conditions on central auditory processing [3,4,5,32,39]. Notably, middle ear diseases were the most prevalent in the APD-diagnosed group (57.58%), reinforcing the hypothesis that early auditory disruptions may contribute to long-term processing deficits. Similar observations have been reported in other populations, including studies from the United States and the UK [2,3]. Environmental factors such as excessive exposure to digitally processed sounds were also noted (14.29% in the risk group), indicating a potential modern contributor to auditory deprivation—an area that warrants further research.

Although developmental risk factors were similarly distributed, their impact on school-related functioning was more pronounced in the APD-diagnosed group. This supports the hypothesis that early vulnerabilities may not manifest fully until later stages of cognitive and auditory development. In both groups, children struggled with phonological processing, especially in reading and writing, as well as with concentration and understanding speech in noise. These challenges align with prior findings linking APD to literacy difficulties, attention deficits, and impaired auditory discrimination [6,24,32].

The high rate of concentration difficulties (78.57% in the risk group, 54.55% in the diagnosed group) suggests overlap between APD and attentional disorders such as ADHD, though these conditions remain distinct. Difficulty understanding speech in noisy environments, reported by over half of both groups, highlights the need for educational accommodations such as classroom FM systems, background noise reduction, and structured auditory training.

These findings emphasize the importance of early screening and intervention, even in children who have not yet received a formal APD diagnosis. A multidisciplinary approach involving audiologists, speech-language therapists, and educators is essential. Intervention programs should focus on phonological awareness, auditory memory, and noise-reduction strategies. While many affected children may benefit from such support, individualized intervention plans remain critical for effective outcomes.

The Risk Assessment Questionnaire (RAQ), developed in response to recurring developmental patterns, offers a potential early screening tool for identifying children at risk of APD. However, as it was developed post hoc and validated on overlapping samples, its screening performance must be confirmed in future research using larger and independent cohorts. The RAQ should be seen as a support tool for early referral, not as a diagnostic instrument.

Overall, the study underscores the significance of early developmental risk factors in APD and the need for systematic school-based support. Further research should continue to explore how these factors evolve and interact over time, ideally through longitudinal and cross-cultural studies using validated tools like the RAQ.

Future work should evaluate the combined use of the RAQ with electrophysiological and neuroimaging measures, such as auditory brainstem response (ABR), frequency-following response (FFR), and, where feasible, fMRI, to improve diagnostic accuracy. It should be noted that such advanced procedures are not part of the standard surdologopedic diagnostic protocol in Poland. The present dataset was derived from routine medical documentation, which included only the basic audiological and higher auditory function tests used for APD diagnosis and referral to therapy. These methods remain beyond standard public diagnostic practice, but will be considered in future, grant-funded studies to better explore the neural mechanisms underlying APD.

Limitations and Future Directions

While this study provides valuable insights, it has several limitations that should be acknowledged. First, the sample size (n = 60) was relatively small, which may limit the generalizability of the findings. Larger-scale studies are needed to confirm these results across more diverse populations.

Second, the study relied on parental interviews, which may introduce recall bias regarding early developmental risk factors. The use of objective medical records and standardized diagnostic tools would strengthen data reliability in future research.

Third, the cross-sectional design limits the ability to track the progression of APD symptoms and changes in risk factors over time. Longitudinal studies are essential to understand developmental trajectories and the impact of early interventions.

Another limitation is the lack of control for potential confounding variables, such as parental education, socioeconomic status, and early digital device exposure. These factors may significantly influence both APD development and school-related difficulties and should be considered in future studies.

Additionally, the RAQ was developed post hoc based on the same clinical dataset used for item selection, increasing the risk of overfitting. Its screening validity should be confirmed through independent samples and larger-scale validation studies.

While the study analyzed developmental and school-related factors, their potential interactions and causal relationships remain underexplored. Future research should examine these links in more depth and assess the predictive value of specific risk indicators.

Finally, cross-cultural comparisons could determine whether similar APD patterns occur in other populations, supporting the broader applicability of findings and tools like the RAQ.

5. Conclusions

This study demonstrates that developmental risk factors such as abnormal muscle tone, recurrent ear infections, and high-risk childbirth are common in both the APD-risk group and the APD-diagnosed group children. However, their impact on academic performance, phonological processing, and attention difficulties becomes more pronounced in children with confirmed APD.

The results emphasize the importance of early identification and preventive interventions, particularly in children with a history of frequent ear infections, speech delays, or learning difficulties. Given the significant overlap between APD and school-related challenges, a multidisciplinary approach involving audiologists, speech therapists, and educators is essential to support affected children.

Future research should focus on longitudinal tracking of APD risk factors, neuroimaging studies, which could provide further insight into the structural and functional differences in auditory processing in children with APD, and comparative analyses with other neurodevelopmental disorders to refine diagnostic and intervention strategies.

The results of this study confirm our initial hypothesis, i.e., developmental risk factors are similarly distributed in both groups, but their impact on school-related difficulties is significantly greater in children with APD. This emphasizes the need for early intervention, even in children who have not received a formal diagnosis yet. Future research should include longitudinal studies to determine whether early intervention in at-risk children can prevent or mitigate school-related difficulties in APD, improving long-term educational outcomes. The findings of this study emphasize the importance of early screening for APD risk factors. The Risk Assessment Questionnaire (RAQ) offers a structured, non-clinical method for identifying children at increased risk of auditory processing difficulties. Further studies are needed to validate its screening performance and determine its potential role within broader APD assessment frameworks.

Since the tool was developed post hoc based on the observed patterns in the same clinical dataset, and then tested on that dataset along with an additional reference group of 30 healthy children, there is a potential risk of overfitting. The control group was recruited after the initial data analysis, specifically to evaluate the questionnaire’s preliminary screening utility. While this design allowed for the identification of relevant risk indicators and initial performance testing, it does not provide an independent validation.

Given the exploratory nature of the RAQ, these results should be considered preliminary. Future research should involve larger, independent cohorts to verify the screening accuracy and generalizability of the questionnaire in broader populations.