Randomised clinical trial: individualised vs. weight‐based dosing of azathioprine in Crohn's disease
Bibliographic record
Abstract
BACKGROUND: Azathioprine (AZA), a pro-drug metabolised to the active metabolites 6-tioguanine nucleotides (6TGN), is a steroid-sparing therapy for Crohn's disease (CD). AIM: To investigate whether AZA therapy is optimised by individualised dosing based on thiopurine methyltransferase (TPMT) activity and 6TGN concentrations. METHODS: This multicentre, double-blind, randomised controlled trial compared the efficacy and safety of weight-based vs. individualised AZA dosing in inducing and maintaining remission in adults and children with steroid-treated CD. The primary outcome was clinical remission (CR) at 16 weeks. In the weight-based arm, subjects received 2.5 mg/kg/day. In the individualised dosing arm, the initial AZA dose was 1.0 mg/kg/day (if intermediate TPMT) or 2.5 mg/kg/day (if normal TPMT). Starting at week 5, the dose was adjusted to target 6TGN concentrations of 250-400 pmol/8 × 10(8) red blood cells (RBC), or to a maximal dose of 4 mg/kg/day. RESULTS: After randomising 50 subjects, the trial was stopped prematurely due to insufficient enrolment. In intention-to-treat analysis, CR rates at week 16 were 40% in the individualised arm vs. 16% in the weight-based arm (P = 0.11). In per-protocol (PP) analysis, week 16 CR rates were 60% in the individualised arm and 25% in the weight-based arm (P = 0.12). At week 16, median 6TGN concentrations in PP remitters and nonremitters were 216 and 149 pmol/8 × 10(8) RBC respectively (P = 0.07). CONCLUSIONS: Despite trends favouring individualised over weight-based AZA dosing, there were no statistically significant differences in efficacy, likely due to low statistical power and inability to achieve the target 6TGN concentrations in the individualised arm. [Clinicaltrials.Gov Identifier Nct00113503].
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".