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Record W2057405699 · doi:10.1097/ta.0b013e3181715d66

Trends in Hospitalization Associated With Traumatic Brain Injury in a Publicly Insured Population, 1992–2002

2009· article· en· W2057405699 on OpenAlexaffabout
Angela Colantonio, Ruth Croxford, Samina Farooq, Audrey Laporte, Peter C. Coyte

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2009
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsMedicineTraumatic brain injuryLogistic regressionInjury preventionPoison controlEmergency medicinePopulationOccupational safety and healthPediatricsInternal medicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability in developed countries. We document trends in TBI-related hospitalizations in Ontario, Canada, between April 1992 and March 2002, focusing on relationships between inpatient hospitalization rates, age, sex, cause of injury, severity level, and in-hospital mortality. METHODS: Information on all acute hospital separations in Ontario with a diagnosis of TBI was analyzed using logistic regression. RESULTS: Hospitalization rates fell steeply among children and young adults but remained stable among adults aged 66 and older. The proportion of TBI hospitalizations with mild injuries decreased from 75% to 54%, whereas the proportion with moderate injuries increased from 19% to 37%. Adjusting for other risk factors, in-hospital deaths were higher for injuries because of motor vehicle crashes than those because of falls. In-hospital death rates were stable for patients with moderate or severe injuries, but increased over time among those whose injuries were classified as mild, suggesting a trend toward more serious injury within the "mild" classification. CONCLUSIONS: Hospitalizations for TBI involve fewer mild injuries over time and are highest in the oldest segment of the population.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.129
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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

Citations80
Published2009
Admission routes2
Has abstractyes

Explore more

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