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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".