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Record W2901327246 · doi:10.1136/heartjnl-2018-313056

Contemporaneous management of valvular heart disease and aortopathy in aircrew

2018· review· en· W2901327246 on OpenAlexaff
Joanna d’Arcy, Thomas Syburra, Norbert Guettler, Eddie D Davenport, Olivier Manen, Gary Gray, Rienk Rienks, Dennis Bron, Edward Nicol

Bibliographic record

VenueHeart · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsAircrewMedicineCardiologyPopulationvalvular heart diseaseRegurgitation (circulation)Context (archaeology)Internal medicineAeronautics

Abstract

fetched live from OpenAlex

Valvular heart disease (VHD) is highly relevant in the aircrew population as it may limit appropriate augmentation of cardiac output in high-performance flying and predispose to arrhythmia. Aircrew with VHD require careful long-term follow-up to ensure that they can fly if it is safe and appropriate for them to do so. Anything greater than mild stenotic valve disease and/or moderate or greater regurgitation is usually associated with flight restrictions. Associated features of arrhythmia, systolic dysfunction, thromboembolism and chamber dilatation indicate additional risk and will usually require more stringent restrictions. The use of appropriate cardiac imaging, along with routine ambulatory cardiac monitoring, is mandatory in aircrew with VHD.Aortopathy in aircrew may be found in isolation or, more commonly, associated with bicuspid aortic valve disease. Progression rates are unpredictable, but as the diameter of the vessel increases, the associated risk of dissection also increases. Restrictions on aircrew duties, particularly in the context of high-performance or solo flying, are usually required in those with progressive dilation of the aorta.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.037
GPT teacher head0.396
Teacher spread0.359 · 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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