P475. What is our success on complex perianal fistulae healing under optimal medical treatment ending up with ileostomy?
Bibliographic record
Abstract
radioimmunoassay, Sanquin Laboratories).PK was analysed by nonlinear mixed-effects modelling and described using a 2-compartiment PK model.All influential covariates were combined into a full model.Results: A total of 734 distinct IFX concentrations measurements were included, comprising data from 324 IBD patients (mean 2.27 measurements).Disease extent was scored based on the Montreal classification for 252 Crohn's disease patients (L1:53/252, L2:79/252, L3: 120/252) and 72 ulcerative colitis patients (E1: 6/72, E2: 26/72, E3:40/72).318/324(98%) of patients were anti-TNF naïve at start of IFX.Mean dose of IFX was 5.49 mg/kg (SD 1.39).ATI were detected in 100/324 (31%) patients.Mean (inter individual variability) values for clearance, central and peripheral volume of distribution were 0.34 L/day (74%), 12.8 L (98%) and 15.1 L (153%).Disease extent did not affect PK.Body weight and anti-IFX antibodies were identified as independent covariates (P<0.001)increasing clearance (mean (SE)) by 2.76 (14.1) fold and 6.04 (10.3) fold respectively, whereas serum albumin had a -0.69 (22.2) fold inverse impact on clearance.Because serum CRP values tended to change rapidly after initiation of treatment, and use of concomitant immunomodulators was often intermittent, these factors could not be evaluated as independent covariates although the administration of continuous concomitant immunomodulators was associated with a decrease in clearance.Conclusions: Antibodies to infliximab, body weight and low serum albumin levels increase clearance 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".