Major depression in multiple sclerosis A population-based perspective
Bibliographic record
Abstract
Abstract—Objective: To determine the prevalence of major depression in multiple sclerosis (MS) in a population-based sample controlling for nonspecific illness effects. Methods: This study used data from a large-scale national survey conducted in Canada: the Canadian Community Health Survey (CCHS). The analysis included 115,071 CCHS subjects who were 18 years or older at the time of data collection. The CCHS interview obtained self-reported diagnoses of MS and employed a brief predictive interview for major depression: the Composite International Diagnostic Interview Short Form for Major Depression. The 12-month period prevalence of major depression was estimated in subjects with and without MS and with and without other long-term medical conditions. Results: The prevalence of major depression was elevated in persons with MS relative to those without MS and those reporting other conditions. The association persisted after adjustment for age and sex (adjusted odds ratio 2.3, 95 % CI 1.6 to 3.3). Major depression prevalence in MS for those in the 18- to 45-year age range was high at 25.7 % (95 % CI 15.6 to 35.7). Conclusions: The prevalence of major depression in the population with MS is elevated. This elevation is not an artifact of selection bias and exceeds that associated with having one or more other long-term conditions. NEUROLOGY 2003;61:1524–1527 Canadian reports of multiple sclerosis (MS) preva-lence have ranged between 85/100,0001 and 217/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".