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Record W2901624254 · doi:10.1136/heartjnl-2018-313059

Congenital heart disease in aircrew

2018· review· en· W2901624254 on OpenAlexaff
Edward Nicol, Olivier Manen, Norbert Guettler, Dennis Bron, Eddie D Davenport, Thomas Syburra, Gary Gray, Joanna d’Arcy, Rienk Rienks

Bibliographic record

VenueHeart · 2018
Typereview
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsAircrewMedicineExpert opinionCivil aviationAviationAviation medicineAeronauticsMilitary aviationWork (physics)Medical emergencyIntensive care medicineEngineeringPathology

Abstract

fetched live from OpenAlex

with suspected or confirmed congenital heart disease (both pilots and non-pilot aviation professionals). It presents expert consensus opinion and associated recommendations and is part of a series of expert consensus documents covering all aspects of aviation cardiology. This expert opinion was born out of a 3 year collaborative working group between international military aviation cardiologists and aviation medicine specialists, as part of a North Atlantic Treaty Organization (NATO) led initiative to address the occupational ramifications of cardiovascular disease in aircrew (HFM-251) many of whom also work with and advise civil aviation authorities.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.350
Teacher spread0.296 · 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
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

Citations5
Published2018
Admission routes1
Has abstractyes

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