Predicting the Need for Supplemental Oxygen During Airline Flight in Patients with Chronic Pulmonary Disease: A Comparison of Predictive Equations and Altitude Simulation
Bibliographic record
Abstract
BACKGROUND: Patients with chronic pulmonary diseases are at increased risk of hypoxemia when travelling by air. Screening guidelines, predictive equations based on ground level measurements and altitude simulation laboratory procedures have been recommended for determining risk but have not been rigorously evaluated and compared. OBJECTIVES: To determine the adequacy of screening recommendations that identify patients at risk of hypoxemia at altitude, to evaluate the specificity and sensitivity of published predictive equations, and to analyze other possible predictors of the need for in-flight oxygen. METHODS: The charts of 27 consecutive eligible patients referred for hypoxia altitude simulation testing before flight were reviewed. Patients breathed a fraction of inspired oxygen of 0.15 for 20 min. This patient population was compared with the screening recommendations made by six official bodies and compared the partial pressure of arterial oxygen (PaO(2)) obtained during altitude simulation with the PaO(2) predicted by 16 published predictive equations. RESULTS: Of the 27 subjects, 25% to 33% who were predicted to maintain adequate oxygenation in flight by the British Thoracic Society, Aerospace Medical Association or American Thoracic Society guidelines became hypoxemic during altitude simulation. The 16 predictive equations were markedly inaccurate in predicting the PaO(2) measured during altitude simulation; only one had a positive predictive value of greater than 30%. Regression analysis identified PaO(2) at ground level (r=0.50; P=0.009), diffusion capacity (r=0.56; P=0.05) and per cent forced expiratory volume in 1 s (r=0.57; P=0.009) as having predictive value for hypoxia at altitude. CONCLUSIONS: Current screening recommendations for determining which patients require formal assessment of oxygen during flight are inadequate. Predictive equations based on sea level variables provide poor estimates of PaO(2) measured during altitude simulation.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".