Measuring psychotic depression
Bibliographic record
Abstract
OBJECTIVE: Psychotic depression (PD) is a highly debilitating condition, which needs intensive monitoring. However, there is no established rating scale for evaluating the severity of PD. The aim of this analysis was to assess the psychometric properties of established depression rating scales and a number of new composite rating scales, covering both depressive and psychotic symptoms, in relation to PD. METHOD: The psychometric properties of the rating scales were evaluated based on data from the Study of Pharmacotherapy of Psychotic Depression. RESULTS: A rating scale consisting of the 6-item Hamilton melancholia subscale (HAM-D6 ) plus five items from the Brief Psychiatric Rating Scale (BPRS), named the HAMD-BPRS11 , displayed clinical validity (Spearman's correlation coefficient between HAMD-BPRS11 and Clinical Global Impression - Severity (CGI-S) scores = 0.79-0.84), responsiveness (Spearman's correlation coefficient between change in HAMD-BPRS11 and Clinical Global Impression - Improvement (CGI-I) scores = -0.74--0.78) and unidimensionality (Loevinger's coefficient of homogeneity = 0.41) in the evaluation of PD. The HAM-D6 fulfilled the same criteria, whereas the full 17-item Hamilton Depression Scale failed to meet criteria for unidimensionality. CONCLUSION: Our results suggest that the HAMD-BPRS11 is a more valid measure than pure depression scales for evaluating the severity of PD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".