Risk of infection-related hospitalizations in people with primary-progressive relative to relapsing-onset multiple sclerosis (P3.353)
Bibliographic record
Abstract
Background / Objective: Little is known about the risk of infection in multiple sclerosis (MS) and whether this differs by MS phenotype. We examined infection-related hospitalizations in people with primary-progressive MS (PPMS) relative to relapsing-onset MS (R-MS). Design/method: We conducted a retrospective cohort study using health administrative data (hospital discharges) linked to the British Columbia MS clinic database. Patients were followed from the first MS clinic visit (‘baseline’ 1996-2008) until death or study end (31st March 2013). The association between MS phenotype and the number of infection-related hospitalizations was examined by a negative binomial regression model. The models were first adjusted for sex, age, disease duration at baseline, and then for disability status as measured by EDSS (0-3; 3.5-5.5; 6+) at baseline. To account for disease modifying drug (DMD) use in R-MS, patients were censored at DMD initiation in a sensitivity analysis. Findings were expressed as adjusted rate ratios (aRR). Results: In total, 939 patients with MS were identified, of whom 59 (6.3[percnt]) had PPMS; baseline EDSS scores were available for 768 (81.8[percnt]). During a mean (SD) follow up of 10.5 (3.0) years, 115 patients were admitted to hospital for at least one infection-related episode (incidence rate= 15.6 (95[percnt]CI 13.2-18.2) per 1000 person-years). The infection rate was higher among those with PPMS as compared with those with R-MS after adjustment for sex, age, and disease duration (aRR 2.2; 95[percnt]CI 1.1 - 4.5) but this association was lost after adjustment for EDSS (aRR 1.4; 95[percnt]CI 0.6 - 2.9). Censoring at DMD initiation did not change the direction of the findings. Conclusion: MS patients with PPMS had a higher hospitalization rate for infections than patients with R-MS but this difference may have reflected higher levels of disability. Findings are highly relevant for the care of PPMS 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".