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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".