Infliximab in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Approximately 20% of patients with ulcerative colitis will require surgical treatment. Recent data suggest that infliximab may reduce the need for surgery in patients with severe ulcerative colitis. However, it is unclear whether data from these small trials will translate to reduced colectomy rates in populations with ulcerative colitis. OBJECTIVE: The purpose of this study was to determine the impact of infliximab on the rates of colectomy for ulcerative colitis and the prescribing practices for infliximab in British Columbia, Canada. DESIGN: We retrospectively reviewed data from 4 province-wide population-based databases maintained by the British Columbia Ministry of Health, a central registry, a hospital separations file, a physician payment file, and a pharmaceutical file. Data were collected from April 1, 2001, to March 31, 2010. SETTINGS: This investigation was conducted at the University of British Columbia. PATIENTS: All patients aged 18 to 75 with ulcerative colitis were included and identified using a validated strategy with International Classification of Diseases 9/10 codes. Patients with severe ulcerative colitis were defined by treatment with a course of corticosteroids during the study period. Patients treated with infliximab were identified using the provincial pharmaceutical file. MAIN OUTCOME MEASURES: The primary outcome was surgery determined by an International Classification of Diseases 9/10 code for partial or total colectomy. RESULTS: Between 2001 and 2010, 7227 subjects were identified with ulcerative colitis. The number of subjects with severe ulcerative colitis was 2537. For general ulcerative colitis, rates of colectomy decreased from 9.97% to 8.88% in the preinfliximab era (2003-2004) and postinfliximab era (2008-2009; p = 0.03). For severe ulcerative colitis, there was no significant difference in colectomy rates (9.97% vs 11.14%; p = 0.18). The highest rate of infliximab prescription was found to be in the provincial health region that encompasses the tertiary academic centers of the province. LIMITATIONS: Although the overall number of patients in this analysis is sizeable, the number of patients who were prescribed infliximab during the study period is relatively modest, which may have impacted trends. CONCLUSIONS: In the severe ulcerative colitis population, there has been no change in the colectomy rate over time despite the introduction of infliximab.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".