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A population bases ED and office visits for paediatric concussion

2012· article· en· W2092923924 on OpenAlexaffabout
Alison Macpherson

Bibliographic record

VenueInjury Prevention · 2012
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsInstitute for Clinical Evaluative SciencesYork University
Fundersnot available
KeywordsConcussionMedicineEmergency departmentIncidence (geometry)PopulationInjury preventionPoison controlOccupational safety and healthSuicide preventionMedical emergencyEmergency medicinePediatricsFamily medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Background Paediatric concussions are an important reason for children to seek care from their physician or in the Emergency Department (ED). Although studies are suggesting that paediatric concussions are increasing for sports-related diagnoses, there is a paucity of population-based information for all causes and including both office and emergency department visits. The objective of this study was to provide population-based estimates of paediatric concussions by age, sex, and external cause of injury. Methods The National Ambulatory Care Reporting System (NACRS) was used to report on children in Ontario, Canada who attended an ED with a discharge diagnosis of concussion (S06) from 2003 to 2006. Children whose physician billed an office visit for a concussion based on the Ontario Health Insurance Program were also included. Results There were over 80 000 children treated for concussions in Ontario over the seven study years. Fifty-two per cent were treated in physician's offices. Boys incurred 66% of all concussions, and the incidence was highest among 14-year-olds to 16-year-olds. The incidence of concussion is increasing, with an increase of 45% in physicians' offices, and an increase of 26% in EDs. Within the ED, 34% of all concussions were due to falls, and 18% were due to hockey or skating. Conclusion Concussions are an important cause of morbidity within the paediatric population in Ontario, and the incidence is increasing. It is important to capture both physician visits and ED visits when calculating incidence. Evidence-based strategies can be used to reduce the burden, with particular emphasis on falls and sports-related concussions.

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 score0.441
Threshold uncertainty score0.233

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 head0.017
GPT teacher head0.332
Teacher spread0.315 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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