Comparing depression screening tools in persons with multiple sclerosis (MS).
Bibliographic record
Abstract
OBJECTIVE: Depression is more common among persons with multiple sclerosis (MS) than the general population. Depression in MS is associated with reduced quality of life, transition to unemployment, and cognitive impairment. Two proposed screening measures for depression in MS populations are the Hospital Anxiety and Depression Scale (HADS) and the Beck Depression Inventory-Fast Screen (BDI-FS). Our objective was to compared the associations of the BDI-FS and the HADS-D scores with history of depressive symptoms, fatigue, and functional outcomes to determine the differential clinical utility of these screening measures among persons with MS. METHOD: We reviewed charts of 133 persons with MS for demographic information; scores on the HADS, BDI-FS, a fatigue measure, and a processing speed measure; and employment status. RESULTS: Structural equation modeling results indicated the HADS-D predicted employment status, disability status, and processing speed more effectively than did the BDI-FS, whereas both measures predicted fatigue. CONCLUSIONS: This study suggests the HADS-D is more effective than the BDI-FS in predicting functional outcomes known to be associated with depression among persons with MS. (PsycINFO Database Record
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".