The Continental Divide: Anti-TNF Use in Pediatric IBD Is Different in North America Compared to Other Parts of the World
Bibliographic record
Abstract
Background and Aims: Use of anti-TNF therapies varies internationally. As an initiative of the international Pediatric IBD Network (PIBDNet), we compared global pediatric IBD anti-TNF practice patterns. Methods: , or related samples Wilcoxon signed rank tests were used to compare groups. Results: 344 physicians treating pediatric IBD responded from 43 countries (54% North America, 29% Europe, 6% Oceania, 6% Asia, 3% Africa, and 2% South America). Respondents treated a median 40 IBD patients. CD was more commonly treated with anti-TNF than UC (40% vs. 10%, p<0.001). North Americans more often used anti-TNF (median 50% vs. 30%, p<0.001) and before immunomodulator (80% vs. 35% CD, p<0.001; 76% vs. 43% steroid-dependent UC, p<0.001). Anti-TNF monotherapy was more common in North America. Anti-TNF in combination with methotrexate, instead of thiopurine, characterized North American practices. North Americans more often continued immunomodulator indefinitely and less often adhered to standard infliximab induction dosing. Access limitations were more common outside North America and Europe for both CD (67% vs. 31%, p<0.001) and UC (62% vs. 33%, p<0.001). Conclusions: Anti-TNF use in North America varies significantly from elsewhere.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".