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Record W2101566811

Cost factors in Canadian pediatric trauma.

2001· article· en· W2101566811 on OpenAlexaffabout
Andrew Dueck, Dan Poenaru, David R. Pichora

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineInjury Severity ScorePediatric traumaLogistic regressionEmergency medicineInjury preventionPoison controlOccupational safety and healthInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To estimate the costs of Canadian pediatric trauma and identify cost predictors. DESIGN: A chart review. SETTING: A regional trauma centre. STUDY MATERIAL: The charts of all 221 children who suffered traumatic injuries with an Injury Severity Score (ISS) of 4 or more seen over 6 years at a regional trauma centre. MAIN OUTCOME MEASURES: Patient data, injury data, all hospital-based costs, excluding nursing, food and medication costs. RESULTS: Mean (and standard deviation) patient age was 12.8 (5) years. Sixty percent were boys. Motor vehicle accidents (MVAs) accounted for 71% of the injuries, followed by falls (11%). The mean (and SD) total cost of care was Can$7,582 (Can$12,370), and the cost of media was Can$2,666. Total cost correlated directly with age (r = 0.29, p < 0.001) and Injury Severity Score (ISS) (r = 0.34, p < 0.001) and inversely with the Pediatric Trauma Score (PTS) (r = -0.20, p = 0.003). The presence of extremity injuries correlated significantly with total cost (r = 0.22, p = 0.001) and PTS (r = -0.25, p < 0.001) but not with the ISS. Logistic regression analysis identified runk injury, ISS and PTS as the main determinants of survival. CONCLUSIONS: The cost of pediatric trauma in Canada can be predicted from admission data and trauma scores. The cost of extremity injuries is significant and can be predicted by the PTS but not the ISS.

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 score0.456
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.065
GPT teacher head0.270
Teacher spread0.205 · 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

Citations30
Published2001
Admission routes2
Has abstractyes

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