Prescriber preference for a particular tumour necrosis factor antagonist drug and treatment discontinuation: population-based cohort
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of physician preference for a particular tumour necrosis factor α (TNF) antagonist on the risk of treatment discontinuation in rheumatoid arthritis. DESIGN: Population-based cohort study. SETTING: British Columbia administrative health data (inpatients, outpatients and pharmacy). PARTICIPANTS: 2742 British Columbia residents who initiated a first course of a TNF antagonist between 2001 and December 2008, had been diagnosed with rheumatoid arthritis, and were treated by 1 of 58 medium-volume to high-volume prescribers. INDEPENDENT VARIABLE: A level of physician preference for the drug (higher or lower) was assigned based on preceding prescribing records of the care-providing physician. Higher preference was defined as at least 60% of TNF antagonist courses initiated in the preceding year. Sensitivity analysis was conducted with different thresholds for higher preference. MAIN OUTCOME MEASURE: Drug discontinuation was defined as a drug-free interval of 180 days or switching to another TNF antagonist, anakinra, rituximab or abatacept. The risk of discontinuation was compared between different levels of physician preference using survival analysis. RESULTS: Higher preference for the prescribed TNF antagonist was associated with improved persistence with the drug (4.28 years (95% CI 3.70 to 4.90) vs 3.27 (2.84 to 3.84), with log rank test p value of 0.017). The adjusted HR for discontinuation was significantly lower in courses of drugs with higher preference (0.85 (0.76 to 0.96)). The results were robust in a sensitivity analysis. CONCLUSIONS: Higher physician preference was associated with decreased risk of discontinuing TNF antagonists in patients with rheumatoid arthritis. This finding suggests that physicians who strongly prefer a specific treatment help their patients to stay on treatment for a longer duration. Similar research on other treatments is warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".