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Record W2094273412 · doi:10.1542/peds.107.6.1298

Effect of Pediatric Surgical Practice on the Treatment of Children With Appendicitis

2001· article· en· W2094273412 on OpenAlexaff
Evan R. Kokoska, Robert K. Minkes, Mark L. Silen, Jacob C. Langer, Thomas F. Tracy, Charles L. Snyder, Patrick A. Dillon, Thomas Weber

Bibliographic record

VenuePEDIATRICS · 2001
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsHospital for Sick Children
FundersAmerican Political Science Association
KeywordsMedicineAppendicitisAcute appendicitisPediatricsGeneral surgery

Abstract

fetched live from OpenAlex

OBJECTIVE: Acute appendicitis in children is managed by both general surgeons (GSs) and pediatric surgeons (PSs). Our objective was to investigate the economics of surgical care provided by either GSs or PSs for appendicitis. METHODS: The outcome of children within our state who underwent operative treatment for appendicitis (January 1994 to June 1997) by board-certified GSs were compared with the results of PSs. Data were sorted according to patient age and diagnosis according to the International Classification of Diseases, Ninth Revision. Analysis of variance was performed on continuous data, and chi(2) analysis was performed on nominal data; data are depicted as mean +/- standard error of the mean. RESULTS: GSs (n = 2178) managed older children when compared with PSs (n = 1018; 11.0 +/- 0.1 vs 9.1 +/- 0.1 years) and less frequently treated perforated appendicitis (18.8% vs 31.9%). Independent of diagnosis (simple or perforated appendicitis), younger children (0-4 years, 5-8 years, and 9-12 years) who were treated by PSs had a significantly shorter hospital stay and/or decreased hospital charge when compared with those who were treated by GSs. However, older children (13-15 years) seemed to have comparable outcomes. CONCLUSIONS: Younger children with appendicitis have reduced hospital days and charges when they are treated by PSs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.011
GPT teacher head0.284
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations64
Published2001
Admission routes1
Has abstractyes

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