A Population-Based Study of Combination vs Monotherapy of Anti-TNF in Persons With IBD
Bibliographic record
Abstract
BACKGROUND: Few data exist about the utilization of combination therapy (anti-tumor necrosis factor [anti-TNF] plus immunosuppressives) in clinical practice. We assessed the prevalence and predictors of combination therapy use vs anti-TNF monotherapy in inflammatory bowel disease (IBD) in the Canadian province of Manitoba. METHODS: All 23 prescribers of anti-TNF medications for IBD in Manitoba facilitated chart review of their comprehensive lists of adult anti-TNF patients from 2005 to 2015. Subjects were stratified by year of first anti-TNF exposure. Patient, disease, and prescriber factors influencing combination therapy use were explored. RESULTS: A total of 774 patients met inclusion criteria. Seventy-one point one percent had Crohn's disease (CD), 28.3% had ulcerative colitis (UC), and 0.6% had IBD unclassified; 45.3% received combination therapy, with no difference between CD and UC. Crohn's disease subjects receiving combination therapy were more likely to have penetrating or perianal disease (56.9% vs 42.8%; P = 0.001) and less likely to have had previous IBD-related surgeries (36.2% vs 46.2%; P = 0.02). The median age at diagnosis and at anti-TNF initiation was lower among combination therapy users. Adalimumab users were as likely as infliximab users to receive combination therapy but persisted with treatment for a shorter time. The proportion of new anti-TNF users receiving combination therapy did not change over time (P = 0.43). There was substantial variation in combination therapy use between prescribers (P = 0.002). The most frequently encountered reasons for avoiding combination therapy were previous intolerance or ineffectiveness of immunosuppressive monotherapy. CONCLUSION: Use of combination therapy has remained unchanged over time despite the publication of high-quality data supporting its efficacy over anti-TNF monotherapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".