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Record W2403693244 · doi:10.1503/cjs.013914

The need for a robust 24/7 subspecialty “clearing house” response for telementored trauma care

2015· article· en· W2403693244 on OpenAlexaffvenue
Andrew W. Kirkpatrick, Douglas R. Hamilton, Andrew Beckett, Anthony J. LaPorta, Susan Brien, Elon Glassberg, Chad G. Ball, Derek J. Roberts, Homer Tien

Bibliographic record

VenueCanadian Journal of Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsCalgary Laboratory ServicesRoyal College of Physicians and Surgeons of CanadaSunnybrook Health Science CentreMcGill University Health CentreCanadian Armed ForcesUniversity of Calgary
Fundersnot available
KeywordsMedicineSubspecialtyPsychological interventionMedical emergencyMajor traumaTrauma careIntensive care medicineNursingFamily medicine

Abstract

fetched live from OpenAlex

Traumatic injury is increasing in importance in all settings and environments worldwide. Many preventable deaths are from conditions that are common and treatable. However, as potentially lethal injuries often induce progressive and frequently irreversible physiologic decline, the timing of interventions is critical. Invasive treatments may need to be offered by prehospital care providers who lack extensive training and practice. Telementoring allows experienced experts to guide less experienced providers remotely using information technology (IT). Early experience has shown that these techniques are practical and considered valuable. Their translation to regular practice, however, will require the immediate availability of appropriately trained remote experts willing to serve as mentors. Acute care trauma specialists are acclimatized to responding to out-of-hospital consultations and assuming overall responsibility for critical physiology and transport and may serve as the backbone of such a national/ international call response initiative.

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)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.162
GPT teacher head0.298
Teacher spread0.136 · 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
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

Citations14
Published2015
Admission routes2
Has abstractyes

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