RHEUMATOID ARTHRITIS: TREATMENT207. IMPACT OF BIOLOGIC THERAPIES ON NEED FOR HIP AND KNEE REPLACEMENT AMONG RHEUMATOID ARTHRITIS AND NON-RHEUMATOID ARTHRITIS PATIENTS IN ONTARIO, CANADA: AN INTERRUPTED TIME-SERIES ANALYSIS
Bibliographic record
Abstract
Background: Rates of orthopaedic surgery among rheumatoid arthritis (RA) patients have been decreasing in several developed countries. One explanatory factor could be the introduction of biologic therapies. While we recently tested this hypothesis using UK data, analyses that account for concurrent trends in non-RA patients is lacking. Our aim here was to estimate the impact of approval of tumour necrosis factor inhibitor therapy (TNFi) for RA on the need for total hip (THR) and total knee replacement (TKR) among incident RA patients in Ontario, Canada, while accounting for any concurrent rate changes among non-RA patients. Methods: Newly diagnosed RA patients (1996–2014) were identified from the Institute for Clinical Evaluative Sciences (ICES) data repository. Those with prior non-RA inflammatory arthritis or a THR/TKR were excluded. Age and sex standardized 5-year incidence rates of THR and TKR were calculated separately for each 6 months between 1996 and 2009. The impact of approval of biologic therapies in 2001 on the level and trend of these outcomes among RA patients was estimated using segmented linear regression, incorporating a 1-year lag period. We also estimated the impact on a time-series of THR and TKR incidence rate ratios (IRRs) comparing RA patients to matched non-RA patients. For each of the outcomes, regression coefficients remaining in final models (p-entry 0.049; p-exit 0.20) were used to estimate an average difference between the post-2001 estimates based on observed values compared to those expected based only on prior-2001 level/trend. We used the midpoint of the biologic-era for this comparison.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".