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Record W3042270457 · doi:10.9778/cmajo.20190200

Death and long-term disability after gun injury: a cohort analysis

2020· article· en· W3042270457 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCMAJ Open · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
KeywordsMedicineHazard ratioPoison controlInjury preventionCohortEmergency medicineOccupational safety and healthPopulationCohort studyEmergency departmentSuicide preventionConfidence intervalMedical emergencyPediatricsPsychiatryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

<h3>Background:</h3> Gun injury accounts for substantial acute mortality worldwide and many others survive with lingering disabilities. We investigated whether additional health losses beyond mortality can also arise for patients who survive with long-term disability. <h3>Methods:</h3> We conducted a population-based individual patient analysis of adults injured by firearms who had received emergency medical care in Ontario, Canada, from Apr. 1, 2002, to Apr. 1, 2019. Longitudinal cohort analyses were evaluated through deterministic linkages of individual electronic patient files. The primary outcome was death or subsequent application for long-term disability in the years after hospital discharge. <h3>Results:</h3> In total, 8313 patients were injured from firearms, of which 3020 were injured from intentional incidents and 5293 were injured from unintentional incidents. A total of 2657 (88.0%) patients with intentional gun injury and 5089 (96.1%) patients with unintentional gun injury survived initial injuries. After a mean 7.75 years of follow-up, patients surviving intentional injuries had a disability rate twice as high as patients surviving unintentional injuries (19.7% v. 10.1%, <i>p</i> &lt; 0.001), equivalent to a hazard ratio of 2.01 (95% confidence interval 1.80–2.25). The higher risk of long-term disability for survivors after intentional gun injury was not explained by demographic characteristics, extended to survivors treated and released from the emergency department, and was observed regardless of whether the incident was self-inflicted or from interpersonal assault. Half of the disability cases were identified after the first year. Additional predictors of long-term disability included a lower socioeconomic status, an urban home location, arrival by ambulance transport, a history of mental illness and a diagnosis of substance use disorder. <h3>Interpretation:</h3> Our study shows that gun death statistics underestimate the extent of health losses from long-term disability, particularly for those with intentional injuries. Additional and sustainable follow-up medical care might improve patient 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.418
Teacher spread0.337 · 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