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Record W3016720670 · doi:10.1186/s13054-020-02846-1

Interfacility helicopter transfers for critically ill patients: always the right choice?

2020· editorial· en· W3016720670 on OpenAlexaff
Hasan Hawilo, Ravi Taneja

Bibliographic record

VenueCritical Care · 2020
Typeeditorial
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCritically illMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

A mechanically ventilated patient was transported by air ambulance from a community hospital intensive care unit (ICU) to a leading tertiary center. Door-to-door travel time was 42 min, which included 12 min airborne and 30 min of transfer time. With only 58 km between the referring and receiving hospital, a ground ambulance under similar conditions would have taken 46 min. While it is intuitive to believe that air is generally quicker and hence better than a ground transfer, was the decision appropriate? How should critical care physicians decide when to use a limited and expensive resource like helicopter emergency medical services (HEMS) for their patients?

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)0.014
metaresearch head score (Gemma)0.074
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 score0.032
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.074
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0050.003
Science and technology studies0.0050.004
Scholarly communication0.0120.007
Open science0.0050.002
Research integrity0.0320.033
Insufficient payload (model declined to judge)0.0140.011

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 head0.023
GPT teacher head0.330
Teacher spread0.308 · 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

Citations6
Published2020
Admission routes1
Has abstractno

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