Measuring the Prevalence of Major Depressive Disorder Among Palliative Patients – Comparing Four Sets of Diagnostic Criteria
Bibliographic record
Abstract
Depression is a common among palliative care patients but the reported prevalence varies in studies. This is due to the overlapping of the conventional diagnostic criteria with the somatic symptoms in palliative patients. Alternative diagnostic criteria for major depressive disorder (MDD) were introduced. However, the standard method to diagnose depression in palliative setting has not been established. The aim of this study is to determine the prevalence of MDD among palliative care patients by comparing between four sets of diagnostic criteria. This is a cross sectional study conducted at two hospitals in Malaysia. Palliative patients were interviewed for presence of depression based on DSM – IV Criteria,Modified DSM – IV Criteria,Cavanaugh Criteria and Endicott's Criteria. They were also requested to complete the Hospital Anxiety and Depression Scale, Distress Thermometer and McGill Quality of Life Questionnaire. The prevalence of MDD among palliative care patients was the highest for Modified DSM – IV Criteria (23.3%), followed by Endicott Criteria (13.8%), DSM – IV Criteria (9.2%) and Cavanaugh Criteria (5%). It was found that 9 items: DSM – IV Criteria Item 1, 2, 3, 4, 6, 7, and 8; and Endicott Criteria Item 6 and 7 have the ability to differentiate between depressed and non depressed patients. The prevalence based on these 9 items (which we named as ‘Revised’ Diagnostic Criteria) is 10.8%. The prevalence of depression varies depending on the criteria used. A ‘revised diagnostic criteria’ was formed for more accurate determination of depression in palliative patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".