Effects of Patient Gender on Clinicians’ Diagnostic Assessment of Youth Disruptive Mood and Behavior
Bibliographic record
Abstract
OBJECTIVE: Youth disruptive behavior disorders (DBDs) have a male preponderance, but the extent to which gender biases in clinical assessment influence this imbalance remains unclear. The present study investigates whether a child patient's gender affects clinicians' diagnostic decision-making regarding Oppositional Defiant Disorder (ODD), Conduct Dissocial Disorder (CDD), and Intermittent Explosive Disorder (IED). METHOD: = 11.09) participated in a global ICD-11 field study. Following an experimental design, participants were asked to use ICD-10 or ICD-11 diagnostic guidelines to evaluate two clinical case vignettes, randomly manipulating the patients' gender (boy, girl) and symptom presentation (ODD-Defiant, ODD-Irritable, CDD, IED). Analyses tested whether clinicians' diagnostic accuracy and perceptions of impairment and severity were affected by the patient's gender. RESULTS: s| = 0.04-0.19). This pattern of nonsignificant differences and negligible/small effect sizes was consistent across all clinical presentations and analyses. CONCLUSIONS: We found no evidence of an association between patient gender, diagnostic accuracy, or perceived severity or impairment when assessing youth DBDs in the present study. Results suggest that diagnostic judgments may be driven by clinical presentation rather than gender and that the male DBD preponderance may not be due to gender diagnostic biases. Further research is needed to replicate these findings among youths in clinical settings, with diverse gender identities, and with other mental health conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".