The ICD‐11 developmental field study of reliability of diagnoses of high‐burden mental disorders: results among adult patients in mental health settings of 13 countries
Bibliographic record
Abstract
Reliable, clinically useful, and globally applicable diagnostic classification of mental disorders is an essential foundation for global mental health. The World Health Organization (WHO) is nearing completion of the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11). The present study assessed inter-diagnostician reliability of mental disorders accounting for the greatest proportion of global disease burden and the highest levels of service utilization - schizophrenia and other primary psychotic disorders, mood disorders, anxiety and fear-related disorders, and disorders specifically associated with stress - among adult patients presenting for treatment at 28 participating centers in 13 countries. A concurrent joint-rater design was used, focusing specifically on whether two clinicians, relying on the same clinical information, agreed on the diagnosis when separately applying the ICD-11 diagnostic guidelines. A total of 1,806 patients were assessed by 339 clinicians in the local language. Intraclass kappa coefficients for diagnoses weighted by site and study prevalence ranged from 0.45 (dysthymic disorder) to 0.88 (social anxiety disorder) and would be considered moderate to almost perfect for all diagnoses. Overall, the reliability of the ICD-11 diagnostic guidelines was superior to that previously reported for equivalent ICD-10 guidelines. These data provide support for the suitability of the ICD-11 diagnostic guidelines for implementation at a global level. The findings will inform further revision of the ICD-11 diagnostic guidelines prior to their publication and the development of programs to support professional training and implementation of the ICD-11 by WHO member states.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".