Toxic‐Metabolic Risk Factors Are Uncommon in Pediatric Chronic Pancreatitis
Bibliographic record
Abstract
To the Editor: We appreciated the comments by Oracz et al (1) regarding their experience in childhood chronic pancreatitis (CP) and specifically toxic-metabolic risk factors. We previously reported toxic-metabolic factors only in 11% of children with CP in our INternational Study Group of Pediatric Pancreatitis: In Search For a CuRE cohort, similar to the frequency reported by Oracz et al (14%). (2) Genetic risk factors were the most common in our study, similar to other pediatric studies. (3–6) Medications, alcohol, smoking, chronic renal failure, hypercalcemia were uncommon in both cohorts (0%–4%), (1,2) but lipid disorders were much higher in the Polish cohort (7.2%) compared with ours (1% with hypertriglyceridemia). Because hypertriglyceridemia is associated with acute pancreatitis, acute recurrent pancreatitis, and possibly CP, (7,8) we recommend defining the lipid disturbances and specifically serum triglyceride levels in their cohort. Multiple risk factors may be found in children with CP, (2) therefore it would be of interest whether Polish children with CP and lipid disturbances had other risk factors including genetic mutations, obstructive/anatomical problems, or other toxic-metabolic risk factors. The study by Oracz et al, (2) along with our work, emphasizes that toxic-metabolic risk factors are relatively uncommon in childhood CP. In children, genetic risk factors predominate (2); in adults toxic-metabolic risk factors (mostly alcohol) (9) are most common. Studies on carefully phenotyped and longitudinally studied cohorts such as INternational Study Group of Pediatric Pancreatitis: In Search For a CuRE have the potential to shed light into the risk factors and pathogenesis of pediatric pancreatitis with improved diagnostics and therapeutics.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".