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Record W3149917754 · doi:10.1097/mpa.0000000000001759

Pediatric Acute Pancreatitis

2021· article· en· W3149917754 on OpenAlexaff
Liron Birimberg‐Schwartz, Sara Rajiwate, Annie Dupuis, Tanja Gonska

Bibliographic record

VenuePancreas · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineAcute pancreatitisParenteral nutritionCohortInternal medicinePancreatitisSubgroup analysisDiseasePediatricsGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the changes over time of pediatric acute pancreatitis (AP) severity, management, and disease outcomes at our academic tertiary center. METHODS: We reviewed 223 pediatric AP admissions (2002-2018) and used a time-to-event regression model to study changes over time. Disease outcomes were analyzed using a subgroup of 89 patients in whom only the AP event determined length of hospital stay and duration of opioid use. RESULTS: There was an increase in mild, but not severe, AP episodes over the examined period. June 2014 was identified as a single cutoff point for change in AP management and disease outcomes independent of each other and of disease severity. Timing of initiating enteral nutrition decreased from 5 to 1.6 days (P < 0.0001) in the entire cohort and from 4.1 to 1.8 days in the subgroup (P = 0.0001) after June 2014. Length of hospitalization decreased from 6 to 3.3 days (P = 0.0008) and days of opioid use from 4.1 to 1.3 (P = 0.002) after June 2014. CONCLUSIONS: Timing of initiating enteral nutrition has significantly reduced at our center after June 2014. In parallel, we observed a significant improvement in disease outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.012
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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