Hospitalization for musculoskeletal disorders in rheumatoid arthritis patients: a population-based study
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) patients failing disease modifying antirheumatic drugs (DMARDs) may undergo anti-Tumor Necrosis Factor (anti-TNF) therapy. Using the Quebec health services administrative databases, we examined the rates of musculoskeletal (MSD)-related hospitalizations among RA patients receiving anti-TNF, DMARDs, and neither of those therapies (non-users). METHODS: Matched cohort analyses were performed separately in 2002-2006 and 2007-2011. In each cohort, DMARD and non-user groups were formed to 3-1 match the anti-TNF users on age, sex, date of RA diagnosis, high-dimensional propensity score and date of the first anti-TNF dispensation (index-date). Non-users did not use DMARDs or anti-TNF drugs during the year before the index-date and in the 90 days post, but used at least one of these medications in the study period. RESULTS: During 2002-2006, 557 anti-TNF users were matched to 1144 DMARD users and to 656 non-users, compared to 690, 1651, and 532 patients, respectively during 2007-2011. The crude rates of MSD-related hospitalizations in the anti-TNF, DMARD and non-users groups were respectively: 8.2/100, 6.4/100 and 10.5/100 patient-years in 2002-2006, and 6.9/100, 4.8/100, and 8.6/100 patient-years in 2007-2011. In multivariable Cox regression models, the hazard ratios of MSD-related hospitalizations (95 % confidence interval) were: 0.95 (0.60; 1.50) for anti-TNF and 0.69 (0.46; 1.02) for DMARD users, versus non-users in 2002-06, and 0.65 (0.37; 1.14) and 0.40 (0.24; 0.66), respectively in 2007-2011. CONCLUSION: The MSD-related hospitalization risk was lower in RA patients using DMARD therapy and similar in those using anti-TNF therapy with or without DMARDs as compared to those not using either of these therapies during the study period.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".