Intercenter variation in initial management of children with Crohnʼs disease
Bibliographic record
Abstract
BACKGROUND: Variation in care is a ubiquitous feature of medical practice and may lead to significant differences in health care costs, quality, and outcomes. We undertook this study to determine the extent of intercenter variation in the initial management of children newly diagnosed with Crohn's disease. METHODS: We analyzed the utilization of 5 classes of medication (immunomodulators, prednisone, antibiotics, 5-aminosalicylates, and infliximab) among 311 children with newly diagnosed Crohn's disease followed at 10 North American pediatric gastroenterology centers. Multivariate logistic regression was used to compare the utilization rate of each class of medication at each of the 10 centers, adjusting for potential confounders including patient age, sex, race, disease severity, and anatomic location of disease. RESULTS: Median utilization of each class of medication was: immunomodulators, 56% (range 29%-97%); prednisone, 78% (range 32%-88%); antibiotics, 29% (range 11%-68%); 5-aminosalicylates, 63.5% (range 18%-92%); and infliximab, 7.5% (range 3%-21%). Each of these treatments showed statistically significant intercenter variation in utilization (P < 0.001 for immunomodulators, prednisone, antibiotics, and 5-ASA; P = 0.02 for infliximab). After adjusting for the demographic and clinical factors listed above, intercenter variation remained significant; however, the low utilization of infliximab precluded multivariate analysis. CONCLUSIONS: Widespread intercenter variation in the medical management of newly diagnosed children with Crohn's disease was observed, even after adjusting for possible differences in case mix between institutions. This variation may lead to unintended differences in health care costs and outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".