Design and Implementation of INSPPIRE
Bibliographic record
Abstract
OBJECTIVES: Acute recurrent pancreatitis (ARP) and chronic pancreatitis (CP) are rare and poorly understood diseases in children. Better understanding of these disorders can only be accomplished via a multicenter, structured, data collection approach. METHODS: The International Study Group of Pediatric Pancreatitis: In Search for a Cure (INSPPIRE) consortium was created to investigate the epidemiology, etiologies, pathogenesis, natural history, and outcomes of pediatric ARP and CP. Patient and physician questionnaires were developed to capture information on demographics, medical history, family and social history, medications, hospitalizations, risk factors, diagnostic evaluation, treatments, and outcome information. Information collected in paper questionnaires was then transferred into Research Electronic Data Capture (REDCap), tabulated, and analyzed. RESULTS: The administrative structure of the INSPPIRE consortium was established, and National Institutes of Health funding was obtained. A total of 14 sites (10 in the United States, 2 in Canada, and 2 overseas) participated. Questionnaires were amended and updated as necessary, followed by changes made into the REDCap database. Between September 1, 2012 and August 31, 2013, a total of 194 children were enrolled into the study: 54% were girls, 82% were non-Hispanic, and 72% were whites. CONCLUSIONS: The INSPPIRE consortium demonstrates the feasibility of building a multicenter patient registry to study the rare pediatric diseases, ARP and CP. Analyses of collected data will provide a greater understanding of pediatric pancreatitis and create opportunities for therapeutic interventional studies that would not otherwise be possible without a multicenter approach.
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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 | 0.080 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".