Impact of Obesity on Pediatric Acute Recurrent and Chronic Pancreatitis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the impact of obesity on pediatric acute recurrent pancreatitis or chronic pancreatitis (CP). METHODS: We determined body mass index (BMI) status at enrollment in INSPPIRE (INternational Study group of Pediatric Pancreatitis: In search for a cuRE) cohort using CDC criteria for pediatric-specific BMI percentiles. We used the Cochran-Armitage test to assess trends and the Jonckheere-Terpstra test to determine associations. RESULTS: Of 446 subjects (acute recurrent pancreatitis, n = 241; CP, n = 205), 22 were underweight, 258 normal weight, 75 overweight, and 91 were obese. The BMI groups were similar in sex, race, and age at presentation. Hypertriglyceridemia was more common in overweight or obese. Obese children were less likely to have CP and more likely to have acute inflammation on imaging. Compared with children with normal weight, obese or overweight children were older at first acute pancreatitis episode and diagnosed with CP at an older age. Obese or overweight children were less likely to undergo medical or endoscopic treatment, develop exocrine pancreatic insufficiency, and require total pancreatectomy with islet autotransplantation. Diabetes was similar among all groups. CONCLUSIONS: Obesity or overweight seems to delay the initial acute pancreatitis episode and diagnosis of CP compared with normal weight or underweight. The impact of obesity on pediatric CP progression and severity deserves further study.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".