Incident depression in patients diagnosed with multiple sclerosis: a multi‐database study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Data on rates of newly diagnosed depression after multiple sclerosis (MS) diagnosis are sparse. Here, incident, treated depression in MS patients after diagnosis compared with matched non-MS patients is described. METHODS: A matched cohort study was conducted in two separate electronic medical databases: the US Department of Defense (US-DOD) military healthcare system and the UK's Clinical Practice Research Datalink GOLD (UK-CPRD). The study population included all patients with a first recorded diagnosis of MS and matched non-MS patients. Patients with a history of treated depression were excluded. Incidence rates and incidence rate ratios with 95% confidence intervals for treated depression after MS diagnosis/matched date were estimated. RESULTS: Incidence rate ratios of treated depression amongst MS patients compared with non-MS patients were 3.20 (95% confidence interval 3.05-3.35) in the US-DOD and 1.90 (95% confidence interval 1.74-2.06) in the UK-CPRD. Incidence rate ratios were elevated across age and sex. Rates were higher in females than males but, compared to non-MS patients, males with MS had a higher relative risk than females with MS. CONCLUSIONS: Multiple sclerosis patients in the UK and the USA have a two- to three-fold increased risk of new, treated depression compared to matched non-MS patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".