Does weight-adjusted anti-tumour necrosis factor treatment favour obese patients with Crohn’s disease?
Bibliographic record
Abstract
BACKGROUND: Adalimumab (ADA) is a subcutaneous anti-tumour necrosis factor (anti-TNF) agent, effective in inducing and maintaining remission in Crohn's disease (CD). Unlike Infliximab (IFX), ADA dosing is not weight adjusted and dose frequency is based on clinical response. AIM: To determine whether obesity is a risk factor for early loss of response (LOR) to anti-TNF treatment and whether weight-adjusted anti-TNF treatment is favourable. MATERIALS AND METHODS: A hospital database of CD patients receiving anti-TNF treatment was analyzed retrospectively. The relationship between time to LOR and BMI was examined by Kaplan-Meier (KM) survival curves and a Cox proportional hazards model. RESULTS: ADA patients: Of the 54 patients (46 BMI<30 and 8 BMI≥30), KM estimation indicated a significantly shorter time to dose escalation in the BMI of at least 30 (χ=6.117, P=0.01). The Cox proportional hazards model showed that an increased hazard of LOR to ADA is related to increases in BMI (P=0.04). IFX patients: Of the 76 patients (62 BMI<30 and 14 BMI≥30), KM estimation showed that the differences in survival curves were not significant (χ=1.933, P=0.16) for the BMI groups. This was supported by the Cox proportional hazard model (P=0.36). CONCLUSION: BMI appears to be important in predicting ADA efficacy (LOR) in CD. IFX appears to overcome this reduction of efficacy in obese patients. A prospective study evaluating the effect of weight on anti-TNF drug response and serum drug levels is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".