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Record W2901390956 · doi:10.1136/heartjnl-2018-313058

Heart muscle disease management in aircrew

2018· review· en· W2901390956 on OpenAlexaff
Joanna d’Arcy, Olivier Manen, Eddie D Davenport, Thomas Syburra, Rienk Rienks, Norbert Guettler, Dennis Bron, Gary Gray, Edward Nicol

Bibliographic record

VenueHeart · 2018
Typereview
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsAircrewMedicineAviation medicineCardiomyopathyHeart diseaseMyocarditisCardiologyIntensive care medicineMedical emergencyPhysical therapyHeart failurePathologyAeronautics

Abstract

fetched live from OpenAlex

This manuscript focuses on the broad aviation medicine considerations that are required to optimally manage aircrew with suspected or confirmed heart muscle disease (both pilots and non-pilot aviation professionals). ECG abnormalities on aircrew periodic medical examination or presentation of a family member with a confirmed cardiomyopathy are the most common reason for investigation of heart muscle disease in aircrew. Holter monitoring and imaging, including cardiac MRI is recommended to confirm or exclude the presence of heart muscle disease and, if confirmed, management should be led by a subspecialist. Confirmed heart muscle disease often requires restriction toflying duties due to concerns regarding arrhythmia. Pericarditis and myocarditis usually require temporary restriction and return to flying duties is usually dependent on a lack of recurrent symptoms and acceptable imaging and electrophysiological investigations.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.351
Teacher spread0.316 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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