P-1228 - A study compared hamilton and calgary depressive scales in assessing depression in schizophrenia
Bibliographic record
Abstract
A review of studies examining the frequency of depression in schizophrenia found prevalence rates ranging from 7% to 75%. This wide range could be reasoned by the in proper selection of the instruments used for assessment. To answer a question; is there a relation between the used depression scale and the category of schizophrenia and/or depression. To compare Hamilton (HAMD) and Calgary (CDRS) depression scales in assessing depression in schizophrenia. 385 schizophrenic patients were recruited from Institute of Psychiatry Ain Shams University hospitals and asked to complete Structured Clinical Interview for Diagnosis section for schizophrenia and depression, socio-demographic sheet, medical history sheet, HAMD, CDRS, and Positive and negative Psychotic Symptoms Scale (PANSS). Depression was found as; disorder (66.2% by HAMD and 39.2% by CDRS), no depression (18.2% by HAMD and 33.2% by CDRS) and symptoms (15.6% by HAMD and 27.5% by CDRS). Agreement of both scales in diagnosing depressive disorder is higher in chronic schizophrenia (62%) than in acute one (50%) and found mostly in continuous and remittent course. No significant correlation was found between CDRS and PANSS scores, while for HAMD, total score for depression was significantly correlated with all psychotic symptoms scores only in schizophrenic patients with depressive disorder. CDRS is more valid in classification of depression categories in schizophrenia than HAMD. It is more sensitive and specific instrument for assessing depression in schizophrenia than HAMD. However, HAMD may be suitable with acute schizophrenia and remittent course.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".