Long distance aeromedical transport post myocardial infarction
Bibliographic record
Abstract
Long distance aeromedical transport of patients post myocardial infarction (MI) occurs with increasing frequency. Despite the benefits of early transport, there are potential risks. Data documenting the frequency of complications are lacking, and guidelines for aeromedical transport post MI are nonexistent. To determine the safety of long distance aeromedical transport post MI and identify risk factors associated with transport-related complications. Analysis of data from a retrospective study of long distance aeromedical transports performed by Montreal-based Skyservice Lifeguard transport service. (A manuscript describing this study has been accepted for publication in the journal Aviation, Space, and Environmental Medicine .) For patients transported by Lear Jet air ambulance post MI, potential risk factors examined included age, gender, Killip class, revascularization procedures, and status at time of transportation (days since admission, chest pain free interval, intravenous medications, and oxygen use). A total of 51 patients were transported by air ambulance during the study period. There were no major complications. Minor inflight complications (ie chest pain, desaturation, or hypotension) occurred in 10% of patients and resolved rapidly with onboard medical intervention. Univariate and multiple logistic regression analysis of the potential risk factors will be presented. Long distance aeromedical transport post MI may be safely performed with a low incidence of minor complications that are easily manageable inflight. Delaying transport 48-72 h after resolution of chest pain reduces the incidence of complications. Practice guidelines for long distance air ambulance transport of post-MI patients need to be established.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".