Search Results (564)

Background and Objectives: Evidence on the acute ergogenic effects of caffeine in adolescent athletes remains limited. This systematic review quantified acute performance-related effects and summarised adverse-event reporting in this population. Methods: PubMed and Web of Science were searched from inception to 13 May 2026 and SPORTDiscus was additionally searched from inception to 15 July 2026 for placebo-controlled studies of acute, dose-defined caffeine ingestion in adolescent athletes aged 10–19 years. Paired Hedges’ g values were synthesised separately across six outcome domains using three-level random-effects models with CR2 cluster-robust inference. Risk of bias and certainty of evidence were assessed using RoB 2 and GRADE. Safety findings were summarised narratively. Results: Thirty-five studies were included, of which 32, representing a total of 726 participants, contributed 363 effect sizes. In the primary analysis, which excluded a priori a small number of extreme combat-sport count-test effects arising from implausibly small reported standard deviations (Hedges’ g > 3.0), caffeine produced a small favourable average effect on physical performance (g = 0.33, 95% CI 0.17 to 0.49). When all effect sizes were included in a sensitivity analysis, the estimate was larger but inflated by these extreme values (g = 0.43, 95% CI 0.20 to 0.67) and was accompanied by substantial heterogeneity (I² = 84%), a prediction interval crossing the null (−0.71 to 1.58), and significant small-study asymmetry. Pooled effects were not statistically significant for sport-specific performance, perceptual response, or physiology. Cognitive findings were based on only two study clusters and were highly uncertain, while psychological outcomes were summarised narratively because the evidence was too sparse and heterogeneous for a meaningful pooled estimate. Exploratory moderator analyses did not provide reliable evidence that effects differed by caffeine dose or ingestion timing. No serious adverse events were reported, but adverse-event assessment was inconsistent. Conclusions: Acute caffeine ingestion may produce a small and uncertain benefit for selected physical-performance tasks in adolescent athletes. Adverse-event reporting was inconsistent, and current evidence is insufficient to support routine caffeine use in youth sport. These findings are specific to acute caffeine exposure under controlled experimental conditions in adolescent athletes and should not be extrapolated to children, non-athlete adolescents, repeated or chronic caffeine use, or energy-drink consumption. Full article

Reliable climate data are essential for hydroclimatic assessment and water-resources management in data-scarce regions. This study evaluated the performance of the WorldClim 2.1 historical weather dataset (WC2.1– CRU-TS v4.09), downscaled and bias-corrected from CRU-TS v4.0 using WorldClim 2.1 climatology, against observed meteorological records within the semi-arid C5 Secondary Drainage Region (C5 SDR; comprising the Riet and Modder River catchments) in central South Africa for the period 1950–2023. Precipitation, maximum temperature (TMAX), and minimum temperature (TMIN) were assessed using statistical performance evaluation metrics, scatter and residual analyses, Innovative Trend Analysis (ITA), Rescaled Adjusted Partial Sums (RAPS), and extreme-event evaluation based on the 95th-percentile threshold. The results showed strong agreement between observed and gridded precipitation records, with correlation coefficients ranging (R) from 0.78 to 0.90 and Nash–Sutcliffe Efficiency (NSE) values between 0.61 and 0.90. Temperature datasets exhibited similarly good performance, with TMAX showing stronger agreement than TMIN. ITA and RAPS analyses demonstrated that the dataset successfully reproduced long-term climatic trends, hydroclimatic regime shifts, and interannual variability observed in station records. Performance varied spatially, with the strongest agreement occurring at lower-elevation stations and comparatively lower performance at stations influenced by localized convective rainfall and topographic variability. Extreme-event analysis revealed that although the dataset effectively reproduced the timing and occurrence of high-rainfall years (R² = 0.974–0.997), it systematically underestimated the magnitude of extreme precipitation events, with percent bias values ranging from −5.5% to −21.0%. In contrast, extreme temperature events were reproduced with very high accuracy and minimal bias. Overall, the WC2.1– CRU-TS v4.09 dataset provides a reliable climatic baseline for hydroclimatic assessments in the C5 SDR. However, caution is required when applying the dataset to analyses sensitive to localized precipitation extremes. The results provide confidence in the use of this dataset for climate characterization, drought assessment, hydrological modeling, ecosystem service evaluation, and future climate-change impact investigations in data-scarce semi-arid environments. Full article

Background/Objectives: Improved preoperative prediction of nodal metastasis in prostate cancer could refine selection for extended pelvic lymph node dissection, a high-morbidity procedure. Sperm-associated antigen 1 (SPAG1) is a candidate marker of nodal status, but its incremental value beyond clinical staging and the associated transcriptional state remain unevaluated. Methods: In The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) cohort (497 patients with matched clinical and RNA-sequencing data), we evaluated the association between SPAG1 expression and pathological N stage by logistic regression with 2000-resample bootstrap optimism correction and sensitivity analyses for missing nodal data and batch effects. Hallmark enrichment analysis compared SPAG1 expression extremes (quartile 4 vs. 1) and, separately, N1 versus N0 tumours adjusted for T stage, Gleason grade, and tissue source site; directional concordance was assessed. Results: N1 rates rose across SPAG1 quartiles from 7.6% to 39.0% (per-quartile odds ratio [OR], 1.83; p = 2.55 × 10⁻⁵). After adjusting for T stage and Gleason grade, SPAG1 remained an independent predictor (adjusted OR, 2.14; 95% confidence interval [CI], 1.50–3.13; p = 4.8 × 10⁻⁵), stable across both sensitivity analyses. Adding SPAG1 improved discrimination (area under the receiver-operating characteristic curve, 0.783 to 0.838; ΔAUC, 0.056; paired DeLong p = 3.03 × 10⁻⁵). The SPAG1 transcriptional programme showed cell-cycle, immune–inflammatory, and mTORC1/TGF-β signalling activation with suppressed differentiation and metabolism; all 15 overlapping Hallmark pathways were directionally concordant with the adjusted N1 signature. Conclusions: SPAG1 expression in primary prostate tumours is a candidate predictor of pathological lymph node metastasis with statistically robust incremental discrimination beyond clinical staging. Independent external validation and biopsy-based feasibility studies are required before clinical application. Full article

Background: Extreme drought and flood events continuously threaten the stability of forest and crop production. Long-term hydrological records derived from tree-ring anatomical proxies provide critical evidence for revealing historical drought hazard differentiation. Methods: Cell wall thickness chronologies of Betula platyphylla (northern forest) and Picea koraiensis (southern agro-pastoral zone) were developed to reconstruct nearly 300-year annual runoff sequences. Pearson correlation, quadratic regression, wavelet transform and superposed epoch analysis (SEA) were applied to quantify hydrological evolution, periodic signals, large-scale climate forcing and statistical coupling between dry/wet extremes and historical yield reduction records. Results: The northern watershed showed stronger interannual runoff oscillation. Both regions entered persistent low-flow phases post-1950. Pacific Decadal Oscillation (PDO) acted as the dominant driver, while solar radiation exerted weak secondary regulation. Severe drought events corresponded closely to historical forest and grain yield losses, with far higher agricultural vulnerability in the southern agro-pastoral ecotone. Conclusions: This study reconstructed the long-term historical runoff of the Greater Khingan Range from the thickness of the cell wall, analyzed the different impacts of PDO on it, and clarified the differentiated effects of drought and flood on agricultural and forestry production losses and the interrelated impact of land use on hydrology and the value of agricultural output. Full article

The monthly CHIRPS ver. 2 gridded rainfall dataset from 1981 to 2016 was employed to analyze distinct precipitation variability patterns and regimes in semi-arid Botswana. An S-mode eigen analysis was performed on the correlation matrix of the rainfall data to extract principal components. The principal component scores (pc-scores) were further rotated using the Varimax eigen analysis method to yield unique precipitation patterns. The rotated pc-scores indicated three separate sub-regions displaying varying precipitation patterns over time. The application of non-hierarchal clustering (K-means) on the pc-scores identified four distinct zones characterized by unique rainfall patterns. A regional frequency study of rainfall in the sub-regions was performed using L-moments. Probabilistic analysis was utilized to model annual rainfall using six common regional frequency analysis probability distribution functions (pdfs): Pearson Type 3 (PE III); three-parameter Weibull; generalized; extreme value (GEV), normal (GNO), logistic (GLO), and Pareto (GPA). The pdfs that demonstrated the optimal correspondence were determined by the goodness-of-fit test, utilizing the Z-statistic. Each cluster displayed unique pdfs and goodness-of-fit pdfs, with the GLO, GEV, GNO, and Weibull offering the most precise representations. Full article

Objective: We aimed to quantify the prognostic value of glucose monitoring-derived time in range (TIR), including continuous glucose monitoring (CGM), flash glucose monitoring (FGM), and fingertip capillary glucose monitoring (FCGM), for predicting adverse clinical outcomes in patients with type 2 diabetes mellitus (T2DM). Research Design and Methods: PubMed, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL, via OVID) were systematically searched from 2017 to November 2025 for studies evaluating the risk of all clinically relevant outcomes associated with different TIRs in T2DM. Extracted data were standardized to evaluate the effect of a 10% increment in TIR. Pooled estimates were calculated using inverse-variance random-effects models incorporating dose–response analysis. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. Results: Twenty-four observational studies involving 20 distinct associations and 35,916 participants were included. Dose–response meta-analyses were conducted for nine associations. The results showed that each 10% increment in TIR was significantly associated with a reduced risk of multiple adverse complications, including all-cause mortality (odd ratio [OR] = 0.88, 95% confidence interval [CI]: 0.82–0.93), vision-threatening diabetic retinopathy (OR = 0.93, 95% CI: 0.87–0.98), diabetic retinopathy (OR = 0.92, 95% CI: 0.89–0.95), lower extremity atherosclerotic disease (OR = 0.86, 95% CI: 0.82–0.91), diabetic peripheral neuropathy (OR = 0.77, 95% CI: 0.71–0.84), and cardiovascular autonomic neuropathy (OR = 0.81, 95% CI: 0.68–0.97). In contrast, the associations for albuminuria (KDIGO [Kidney Disease: Improving Global Outcomes] A2 and A3) and amputation did not reach statistical significance in the primary meta-analysis. Conclusions: In conclusion, each 10% increment in TIR is consistently associated with a reduced risk of mortality and various micro- and macrovascular complications in T2DM. These findings suggest TIR as a robust prognostic indicator and actionable therapeutic target in diabetes management. Full article

Sulfur hexafluoride (SF₆) is a typical long-lived greenhouse gas that has attracted considerable attention due to its extremely long atmospheric lifetime and high global warming potential. In this study, atmospheric SF₆ column concentrations from 2023 to 2025 were retrieved using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing observations at the Hefei site, China. The seasonal variation and annual trends of SF₆ were analyzed, and the potential influencing regions and transport characteristics of high-value events were investigated by combining wind direction statistics with backward trajectory clustering analysis. The results show that high SF₆ column concentrations over Hefei mainly occurred in summer, while relatively low concentrations appeared in winter during the observation period from 2023 to 2025. The average total column concentration of SF₆ is 2.80 × 10¹⁴ molec·cm⁻², the average dry-air column-averaged mole fraction is 13.06 ppt, and the annual growth rate is 0.88 ppt yr⁻¹. The high-value events of SF₆ at the Hefei site are mainly concentrated in summer, and the wind direction mainly corresponds to the northeast and the southeast wind. The air masses with high concentrations of SF₆ mainly originated from the transportation path in the nearby southern regions of the station. During summer, the season with relatively higher SF₆ concentrations, the air masses were mainly southerly and easterly winds. The results of this study provide observational evidence and scientific support for understanding the characteristics of variation in atmospheric SF₆ column concentrations over Hefei and the influence of regional transport on high SF₆ values. Full article

Satellite-based precipitation products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG, V07) provide global coverage but exhibit systematic biases in daily accumulations, particularly for extreme events. This study presents a hybrid bias-correction framework (LSEQM+DL) for daily satellite precipitation that sequentially integrates Linear Scaling (LS) for mean bias, Empirical Quantile Mapping (EQM) with a Generalized Pareto Distribution (GPD) tail adjustment for distributional alignment, and a Convolutional Neural Network (CNN) refinement that targets extreme-precipitation pixels. A station-density confidence mask scales the deep-learning influence with gauge density, so the CNN refinement is strongest where the reference, the CPC Unified Gauge-Based Analysis of Daily Precipitation (CPC-UNI), is best constrained. The framework targets the IMERG Late Run (IMERG-L), whose roughly 14 h latency suits near-real-time flood monitoring. It is applied over Indonesia (2001–2025) and evaluated against CPC-UNI and 171 independent stations of the Meteorological, Climatological, and Geophysical Agency (BMKG) through three pillars: adjusting values, aligning distributions, and preserving extremes. At independent stations, the correction brings the standard deviation ratio from 0.71 (LS) to 1.00, the relative bias from $-$11.4% to $-$0.6%, and the 99th-percentile ratio from 0.71 to 1.01, and reduces a 21% over-estimation of wet-day frequency to within 5% of that observed. These gains carry a designed cost: the probability of detection falls from 0.78 to 0.65, while pixel-level temporal metrics (correlation, root-mean-square error, Nash–Sutcliffe efficiency) remain largely unchanged, confirming that the framework improves statistical properties rather than day-to-day timing. Relying only on globally available satellite and gauge-analysis data, and degrading gracefully where gauges are sparse, the framework is portable in principle with regional recalibration of its three tuning parameters. The corrected near-real-time product, with its station-density mask as a spatially explicit quality indicator, is intended to support flood monitoring, water resource management, and climate risk assessment in Indonesia and other gauge-sparse tropical regions. Full article

Unmanned aerial vehicle (UAV) multispectral imagery provides a promising approach for field-scale retrieval of soil organic matter (SOM) during the bare-soil period. However, tillage-induced surface heterogeneity is often overlooked. This heterogeneity may alter soil spectral responses and model performance. This study examined the effects of surface tillage status on UAV-based SOM retrieval in farmland. UAV multispectral imagery and 108 topsoil samples were collected during the bare-soil period. The SOM values ranged from 1.37 to 30.95 g/kg. Analyses were conducted under three tillage-status settings: undifferentiated tillage status, plowed-leveled status, and plowed-unleveled status. Spectral and textural features were extracted and selected using a genetic algorithm. These features were then used to develop SOM retrieval models with random forest regression, extreme gradient boosting, and support vector regression. For the six original multispectral bands, the correlations between SOM and band reflectance differed among tillage-status settings. They were weak under the undifferentiated tillage status. They were significantly negative under the plowed-leveled status and significantly positive under the plowed-unleveled status. Texture-derived indicators and standard normal variate analysis suggested that the positive correlations under the plowed-unleveled status may be partly associated with surface-structure-related spectral amplitude effects. Integrating textural features improved the overall test-set accuracy metrics. However, statistically detectable reductions in absolute prediction error were mainly observed under the plowed-unleveled status. On the random-split held-out test set, the highest $R^2$ values reached 0.84 and 0.85 under the plowed-leveled and plowed-unleveled statuses, respectively. These results indicate that surface tillage status is an important source of surface heterogeneity. It should therefore be explicitly considered in UAV-based SOM retrieval under the present study conditions. Full article

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

Lifetime data arising in engineering reliability, survival analysis, actuarial science, and environmental studies often exhibit substantial right-skewness, extreme observations, and heterogeneous hazard-rate structures. Classical lifetime distributions may not adequately capture these characteristics, thereby affecting risk assessment, reliability evaluation, and predictive performance. In this paper, we introduce the Heavy-Tailed QLindley (HTQL) distribution, a new two-parameter heavy-tailed extension of the QLindley model obtained through the New Family of Heavy-Tailed (NFHT) transformation. The proposed distribution provides greater flexibility for modelling positively skewed and heavy-tailed data while preserving analytical tractability. The HTQL model accommodates increasing, decreasing, bathtub-shaped, unimodal, and nearly constant hazard rate functions, making it suitable for applications in reliability analysis, survival studies, actuarial science, and environmental modelling. Several mathematical and statistical properties of the HTQL distribution are derived, including explicit expressions for the quantile function, ordinary and incomplete moments, order statistics, and reliability measures. Important tail-based risk measures such as Value-at-Risk, Tail Value-at-Risk, Tail Variance, Tail Variance Premium, and Expected Shortfall are also obtained. Parameter estimation is investigated using maximum likelihood, ordinary least squares, Cramér–von Mises, and Bayesian approaches, together with bootstrap confidence intervals. A Monte Carlo simulation study is conducted to evaluate the finite-sample performance of the proposed estimators. The practical usefulness of the HTQL distribution is illustrated using three real-world datasets from pharmacokinetics, engineering reliability, and environmental studies. The empirical results show that the HTQL distribution provides highly competitive fits compared with several classical, Lindley-type, and heavy-tailed distributions. Overall, the proposed model constitutes a flexible and parsimonious alternative for modelling positive heavy-tailed data. Full article

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R² = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

Copulas, as a new tool for statistical analysis, are studied in depth. One of the most notable aspects of this study is the geometric perspective, particularly the concept of regeneration via extreme points and the well-known result in functional analysis: the Krein–Milman theorem. The practical value of such a theoretical study is highlighted by a very interesting illustration in biomedical analysis. As the effective determination of all extremal points is quasi-impossible, we suggest an implementation method to approximate those of a finite and countable set. Computer implementations have demonstrated the effectiveness of the adopted approach. Full article

Background: This study aimed to compare the measurement agreement and intramethod reliability of four cephalometric analysis workflows: manual tracing, semi-automated digital analysis (Dolphin), fully automated AI-based analysis (WebCeph), and expert-adjusted AI analysis (WebCeph+). Methods: In this retrospective method-comparison study, 67 lateral cephalometric radiographs were initially included. After the exclusion of radiographs containing extreme values, 54 radiographs (35 females, 19 males; mean age: 15.0 ± 2.13 years) were analyzed. Twenty-one skeletal, dental, and soft-tissue parameters (13 angular, 8 linear) were evaluated across the four methods. Intramethod repeatability was assessed via the intraclass correlation coefficient (ICC). Intermethod comparisons were analyzed using ANOVA and post hoc pairwise tests. Pragmatic clinical relevance thresholds were predefined as ±2 degrees for angular measurements and ±2 mm for linear measurements. Results: All methods demonstrated high intramethod reliability, with ICC values exceeding 0.90 in 20 out of 21 parameters. Manual and Dolphin methods yielded statistically comparable results (p > 0.05). In contrast, WebCeph differed significantly from manual and/or Dolphin in seven parameters, including SNA, IMPA, Go-Gn length, Pog to N-perpendicular, Wits appraisal, nasolabial angle, and mentolabial angle (p < 0.05). Several discrepancies exceeded the predefined pragmatic thresholds (±2 degrees and ±2 mm), highlighting their potential clinical relevance. After expert adjustment (WebCeph+), statistically significant inter-workflow differences were no longer observed; however, residual individual-level variability remained for selected parameters. Conclusions: Fully automated WebCeph analysis showed limited agreement with manual and semi-automated methods for several clinically relevant measurements. Expert adjustment reduced systematic mean discrepancies and improved agreement with clinician-dependent workflows; however, residual individual-level variability remained for selected parameters. AI-driven cephalometric analysis should therefore be considered a supportive tool requiring specialist verification rather than an unsupervised replacement for conventional methods. Full article