Prodromal phase of multiple sclerosis: evidence from sickness absence patterns before disease onset – a matched cohort study
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the prodromal phase of multiple sclerosis (MS) by investigating annual sickness absence rates before MS onset. METHODS: A retrospective cohort study was conducted using Sweden's linked clinical and health administrative data. We identified MS cases via a validated algorithm using International Classification of Diseases (ICD) diagnostic codes for MS ('administrative cohort') or registration in the Swedish MS registry ('clinical cohort'). MS onset was defined as the first MS/demyelinating disease ICD code (administrative cohort) or, for the clinical cohort, MS symptom onset date, if earlier. Cases were matched with up to five controls from the general population with no MS/demyelinating disease history. Yearly sickness absence rates up to 18 years pre-MS onset were compared using negative binomial regression with generalised estimating equations. RESULTS: The administrative/clinical cohorts comprised 8618/6361 MS cases and 43 072/31 776 controls. Sickness absence rate ratios were significantly elevated from 6 years before MS onset in the administrative cohort and 2 years before in the clinical cohort. The adjusted rate ratios peaked in the year pre-MS onset, reaching 2.59 (95% CI 2.40 to 2.79) in the administrative cohort and 1.19 (95% CI 1.06 to 1.34) in the clinical cohort. We also observed age-related and sex-related differences primarily in the year before MS onset, with males and older individuals exhibiting higher rate ratios. CONCLUSIONS: We observed a significant increase in sickness absence spells in individuals on the path to developing MS. Investigating sick leave patterns may provide a unique and broad perspective on the health trajectories of chronic conditions like MS.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".