Biopsychosocial correlates of lifetime major depression in a multiple sclerosis population
Bibliographic record
Abstract
The objective of this paper was to evaluate the lifetime and point prevalence of major depression in a population-based Multiple Sclerosis (MS) clinic sample, and to describe associations between selected biopsychosocial variables and the prevalence of lifetime major depression in this sample. Subjects who had participated in an earlier study were re-contacted for additional data collection. Eighty-three per cent (n=136) of those eligible consented to participate. Each subject completed the Composite International Diagnostic Interview (CIDI) and an interviewer-administered questionnaire evaluating a series of biopsychosocial variables. The lifetime prevalence of major depression in this sample was 22.8%, somewhat lower than previous estimates in MS clinic populations. Women, those under 35, and those with a family history of major depression had a higher prevalence. Also, subjects reporting high levels of stress and heavy ingestion of caffeine (>400 mg) had a higher prevalence of major depression. As this was a cross-sectional analysis, the direction of causal effect for the observed associations could not be determined. By identifying variables that are associated with lifetime major depression, these data generate hypotheses for future prospective studies. Such studies will be needed to further understand the etiology of depressive disorders in MS.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".