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Record W2901418278 · doi:10.1136/heartjnl-2018-313057

Management of cardiac conduction abnormalities and arrhythmia in aircrew

2018· review· en· W2901418278 on OpenAlexaff
Norbert Guettler, Dennis Bron, Olivier Manen, Gary Gray, Thomas Syburra, Rienk Rienks, Joanna d’Arcy, Eddie D Davenport, Edward Nicol

Bibliographic record

VenueHeart · 2018
Typereview
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsAircrewMedicineCardiac arrhythmiaAviation medicineCardiac electrophysiologyCardiologyIntensive care medicineAeronauticsInternal medicinePathologyAtrial fibrillationElectrophysiology

Abstract

fetched live from OpenAlex

often operate within a demanding physiological environment, that potentially includes exposure to sustained acceleration (usually resulting in a positive gravitational force, from head to feet (+Gz)) in high performance aircraft. Aeromedical assessment is complicated further when trying to discriminate between benign and potentially significant rhythm abnormalities in aircrew, many of whom are young and fit, have a resultant high vagal tone, and among whom underlying cardiac disease has a low prevalence. In cases where a significant underlying aetiology is plausible, extensive investigation is often required and where appropriate should include review by an electrophysiologist. The decision regarding restriction of flying activity will be dependent on several factors including the underlying arrhythmia, associated pathology, risk of incapacitation and/or distraction, the type of aircraft operated, and the specific flight or mission criticality of the role performed by the individual aircrew.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.332
Teacher spread0.300 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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