Challenges in screening for depression in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Screening has frequently been proposed as a strategy for detection of depression in multiple sclerosis (MS). In a recent study, we found a minimal impact of screening, even when this was coupled with rapidly responsive and evidence-based depression care. METHODS: In order to explore the challenges involved in screening we analyzed prospective data from the Canadian Impact of MS (CIMS) database, which provides annual ratings on a self-report depression rating scale, the Center for Epidemiologic Studies Depression Rating Scale (CES-D). RESULTS: Approximately 30% of respondents screened positive at each visit. CES-D ratings correlated fairly strongly from year to year, Pearson's r ranged from 0.65 to 0.73. Approximately 10% of those below the CES-D cut-point at each assessment exceeded the cut-point when rated 1 year later, but only about half of these cases had large (≥10 points) increases in their scores. CONCLUSIONS: Screening interventions are generally oriented towards early detection, whereas the longitudinal pattern of depressive symptoms in MS appears to be characterized more prominently by a persistent burden of depressive symptoms in a substantial proportion of the population. Resources invested in screening efforts can probably be more effectively deployed in other areas, such as improved long-term clinical management.
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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 | 0.057 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".