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

Heart muscle disease management in aircrew

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.035
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