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Record W2010867402 · doi:10.1136/ip.2010.029215.803

Systems saving lives: a structured review of trauma systems and their impact on mortality

2010· review· en· W2010867402 on OpenAlexaff
Alexandra M. Harrington, Avery B. Nathens, Natalie Yanchar, Hala Tamim, Ian Pike, Alison Macpherson

Bibliographic record

VenueInjury Prevention · 2010
Typereview
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineInjury preventionMajor traumaPoison controlOccupational safety and healthMedical emergencyHuman factors and ergonomicsCause of deathSuicide preventionIntensive care medicine

Abstract

fetched live from OpenAlex

Background For Canadians under the age of 45, injury is the leading cause of death and remains the leading cause of potential years of life lost for those up to the age of 70. Coordination of health services, particularly trauma systems, has the potential to improve treatment and management of the victims. Some research has suggested that trauma systems are associated with a reduction in preventable deaths for injured patients, a reduction in gross mortality, and a reduction in motor vehicle-collision mortality. With the support for the regionalisation of trauma services growing information regarding the effectiveness of trauma systems themselves is key. Objective To conduct a systematic review of the effectiveness of trauma systems in reducing mortality. Methods We will use the method outlined by the Cochrane Collaboration, and conduct a systematic review of the literature using the following key words: Trauma systems, mortality and injury. To date, 1665 potential articles have been identified including 200 review articles. Articles are currently being screened and will be reviewed when they include relevant evidence regarding traumas systems and mortality for both the adult and paediatric populations. Relevance This work will provide information to inform policies related to the development and maintenance of trauma systems.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
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 head0.067
GPT teacher head0.411
Teacher spread0.344 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2010
Admission routes1
Has abstractyes

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