The impact of ethnicity on delays in initiating advanced therapy for inflammatory bowel disease
Bibliographic record
Abstract
OBJECTIVES: Prompt initiation of advanced therapy medications, encompassing biologics and small-molecule treatments, is crucial for the effective management of inflammatory bowel disease (IBD). The time taken from the decision to start an advanced therapy to the first administration, or time to advanced therapy (TAT), can vary significantly between individuals and negatively affect disease course; however, our knowledge of the causes of variation in TAT is poor. We aimed to investigate the impact of demographic factors on delays in TAT. METHODS: A retrospective study, conducted at a tertiary IBD referral centre, analysed electronic patient records of 1298 patients with IBD, and collected data on the TAT for their index advanced therapy. The variables studied included disease type, treatment, age, sex, ethnicity, and socioeconomic status, using index of multiple deprivation. Multiple negative binomial regression was performed to assess the relative effects of these variables on TAT. RESULTS: TAT was significantly longer in the non-White ethnicity group ( P = 0.039). Patients of Black ethnicity had an incident rate ratio (IRR) of 1.46 [95% confidence interval (CI): 1.09-1.95], for mixed ethnicity IRR = 1.26 (95% CI: 0.77-2.05) and for Asian IRR = 1.17 (95% CI: 0.96-1.41) compared with White patients. Adalimumab was also associated with a longer TAT ( P ≤ 0.001; IRR = 1.37; 95% CI: 0.95-1.96). CONCLUSION: Non-White ethnicity is associated with a longer TAT, as is treatment with adalimumab which may be because of outsourcing of medication supply. Further research on the causes and strategies to address this health disparity is required.
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".