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Record W2610165645 · doi:10.1111/jebm.12256

Prehospital notification for major trauma patients requiring emergency hospital transport: A systematic review

2017· review· en· W2610165645 on OpenAlexaff
Anneliese Synnot, Adrian Karlsson, Lisa Brichko, Melissa J. Chee, Mark Fitzgerald, Mahesh C. Misra, Teresa Howard, Joseph Mathew, Thomas Rotter, Michelle Fiander, Russell L. Gruen, Amit Gupta, Satish Dharap, Madonna Fahey, Michael Stephenson, Gerard O’Reilly, Peter Cameron, Biswadev Mitra

Bibliographic record

VenueJournal of Evidence-Based Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsSurgical Specialties (Canada)University of Saskatchewan
FundersDepartment of Industry and Science, Australian GovernmentAustralia-India Strategic Research FundDepartment of Industry, Innovation and Science, Australian GovernmentDepartment of Science and Technology, Ministry of Science and Technology, IndiaAustralian Government
KeywordsMedicineObservational studyPsychological interventionEmergency medicineEmergency medical servicesOdds ratioConfidence intervalMajor traumaEmergency departmentMedical emergencyInternal medicineNursing

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewmedium
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.331
GPT teacher head0.456
Teacher spread0.125 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2017
Admission routes1
Has abstractyes

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