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Record W2515955095 · doi:10.7759/cureus.774

Emergency Medicine in Remote Regions

2016· editorial· en· W2515955095 on OpenAlexaffabout
Tia Renouf, Megan Pollard

Bibliographic record

VenueCureus · 2016
Typeeditorial
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineNova scotiaFixed wingSnapshot (computer storage)Medical emergencySubmarine pipelineAeronauticsMedical educationOceanographyWingEngineering

Abstract

fetched live from OpenAlex

Rural and remote places like Sable Island (Nova Scotia) or François (Newfoundland) pose a challenge in delivering both health care and appropriate education that today's learners need to practice in a rural setting. This education can be difficult to deliver to students far from academic centers. This is especially true for learners and practitioners at offshore locations like ships, oil installations, or in the air when patients are transported via fixed wing aircraft or helicopter. The following editorial provides a snapshot of the setting and the challenges faced while working as a physician on a ship, in remote regions.

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 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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0060.005
Open science0.0020.002
Research integrity0.0130.018
Insufficient payload (model declined to judge)0.0070.004

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 head0.047
GPT teacher head0.375
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2016
Admission routes2
Has abstractyes

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