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Record W2037758450 · doi:10.1097/bot.0000000000000105

The World Health Organization's Action Plan on the Road Traffic Injury Pandemic

2014· review· en· W2037758450 on OpenAlexaff
Paul J. Moroz, David A. Spiegel

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2014
Typereview
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineMedical emergencyDeveloping countryPandemicAction planHealth careLow and middle income countriesCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

Road traffic crash-related death, injury, and chronic disability continue to be a major worldwide burden to drivers, pedestrians, and users of mass transit, especially in low- and middle-income countries (LMIC). Projections predict worsening of this burden, and while motorization of LMIC increases exponentially, a corresponding improvement in prehospital and acute in-hospital trauma care has not been seen. The WHO now has 2 programs that address different elements of this challenge, namely, the Violence and Injury Prevention department (prevention) and the Emergency and Essential Surgical Care project (treatment). Activities of Violence and Injury Prevention have included developing guidelines for prehospital and essential trauma care, whereas activities of the Emergency and Essential Surgical Care have included developing the Integrated Management of Emergency and Essential Surgical Care toolkit and a textbook, "Surgical Care at the District Hospital." Organized surgical institutions in high-income countries-trauma associations, university departments, surgical nongovernmental organizations, etc.-can benefit from the infrastructure and tools the WHO has developed to better address the deficits in surgical services to improve the equitable distribution of surgical care services and resources to LMIC.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.085
GPT teacher head0.375
Teacher spread0.290 · 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 designNot applicable
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

Citations21
Published2014
Admission routes1
Has abstractyes

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