The diagnostic validity of depression scales and clinical judgement in the Kurdistan region of Iraq
Bibliographic record
Abstract
We aimed to find the depression rating scale with the greatest accuracy when applied by psychiatrists in Iraqi Kurdistan. We recruited 200 patients with primary depression and 200 controls living in the Kurdistan region of Iraq. The Mini International Neuropsychiatry Inventory (MINI) was used as a gold standard for DSM-IV depression. We also used: the two-item and the nine-item versions of the Patient Health Questionnaire (PHQ2, PHQ9), the Hospital Anxiety and Depression Scale (HADS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Centre for Epidemiological Studies Depression (CES-D) scale. Interviews were performed by psychiatrists who also rated their clinical judgement using the Clinical Global Impression (CGI) scale and other mental health practitioners. All scales and tools performed with high accuracy and reliability. The least accurate tool was the PHQ2; however, with only two items it was efficient. Sensitivity and specificity for all tools were above 90%. Clinicians using the CGI were accurate in their clinical judgement. The CDSS appeared to be the most accurate scale for DSM-IV major depression and the PHQ2 the most efficient. However, only the CDSS appeared to offer an advantage over psychiatrists' judgement.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".