Infection-related health care utilization among people with and without multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Little is known about infection risk in multiple sclerosis (MS). OBJECTIVE: We examined infection-related health care utilization in people with and without MS. METHODS: Using population-based health administrative data from British Columbia, Canada, people with MS were followed from their first demyelinating claim (1996-2013) until death, emigration, or study end (2013). Infection-related hospital, physician, and prescription data of MS cases were compared with sex-, age-, and geographically matched controls using adjusted regression models. Sex and age differences (18-39, 40-49, 50-59, 60+ years) were explored. RESULTS: Relative to 35,837 controls, 7179 MS cases were over twice as likely to be hospitalized for infection (adjusted odds ratio: 2.39; 95% confidence interval (CI): 2.16-2.65), had 41% more physician visits (adjusted rate ratio (aRR): 1.41; 95% CI: 1.36-1.47), and filled 57% more infection-related prescriptions (aRR: 1.57; 95% CI: 1.49-1.65). Utilization was disproportionately higher in MS men than women and was elevated across all ages. MS cases had nearly twice as many physician visits and two to three times more hospitalizations for pneumonia, urinary system infections, and skin infections (aRRs ranged from 1.6 to 3.3) and over twice as many hospitalizations for intestinal infections (aRR = 2.6) and sepsis (aRR = 2.2). CONCLUSION: Infection-related health care utilization was increased in people with MS across all age groups, with a higher burden for men.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".