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Record W2900798618 · doi:10.1136/heartjnl-2018-313052

Assessing aeromedical risk: a three-dimensional risk matrix approach

2018· review· en· W2900798618 on OpenAlexaff
Gary Gray, Dennis Bron, Eddie D Davenport, Joanna d’Arcy, Norbert Guettler, Olivier Manen, Thomas Syburra, Rienk Rienks, Edward Nicol

Bibliographic record

VenueHeart · 2018
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsCanadian Armed Forces
Fundersnot available
KeywordsMedicineRisk assessmentRisk managementRisk analysis (engineering)AviationMedical emergencyComputer scienceComputer securityEngineering

Abstract

fetched live from OpenAlex

from air operations with any identifiable medical risk, and led to frequent medical disqualification. The concept of considering aeromedical risk as part of the spectrum of risks that could lead to aircraft accidents (including mechanical risks and human factors) was first proposed in the 1980s and led to the development of the 1% rule which defines the maximum acceptable risk for an incapacitating medical event as 1% per year (or 1 in 100 person-years) to align with acceptable overall risk in aviation operations. Risk management has subsequently evolved as a formal discipline, incorporating risk assessment as an integral part of the process. Risk assessment is often visualised as a risk matrix, with the level of risk, urgency or action required defined for each cell, and colour-coded as red, amber or green depending on the overall combination of risk and consequence. This manuscript describes an approach to aeromedical risk management which incorporates risk matrices and how they can be used in aeromedical decision-making, while highlighting some of their shortcomings.

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 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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.013

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 head0.248
GPT teacher head0.570
Teacher spread0.322 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2018
Admission routes1
Has abstractyes

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