MétaCan
Menu
Back to cohort

CO<sub>2</sub> Narcosis as a Root Cause of Unexplained Physiological Events in High Performance Aircraft

2020· preprint· en· W3016704979 on OpenAlexaff
Oleg Bassovitch

Bibliographic record

VenuePreprints.org · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsAcidosisAnesthesiaMedicineRespiratory acidosisAtelectasisHypercapniaHypoxia (environmental)Ventilation (architecture)OxygenInternal medicineChemistryEngineeringLung

Abstract

fetched live from OpenAlex

Since 1991, there has been an alarming increase in the number of unexplained physiological events (UPEs) reported and experienced by pilots of jet fighters across different fleets. The UPEs have resulted in grounding some airframes, loss of aircraft, and even loss of life. There is no single agreed-upon root cause of UPEs that has been identified, and therefore there is no reliable corrective action. This author analyzed the literature related to other industries where artificial hyperoxic gas mixes are employed and where similar adverse reports have been reported. I hypothesize that UPEs are caused by high-dose oxygen delivery in excess of officially approved oxygen schedules while airflow rates are often inadequate, at a time when the positive pressure breathing feature of their oxygen regulator is not used. In a setting where pulmonary vital capacity is adversely affected by G-maneuvers and oxygen- and G-induced atelectasis, tidal volume is reduced by flight gear, and effective gas exchange is not supported by adequate ventilation, these factors combine to produce respiratory acidosis, followed by acute respiratory distress syndrome, CO2 narcosis, and coma. Reports from field data related to incidents in F-18S/H, showing that emergency oxygen did not correct the hypoxia-like symptoms including long-lasting periods of incapacitation and prolonged headaches, lend support to our hypothesis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.006

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 head0.194
GPT teacher head0.445
Teacher spread0.251 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePreprints.orgSame topicOccupational Health and PerformanceFrench-language works237,207