Air travel in chronic lung disease: can clinical variables predict response to the hypoxic challenge test?
Bibliographic record
Abstract
Introduction: In COPD, a sea-level SpO2>95% predicts a small risk of positive hypoxic challenge test (HCT), but data are scarce in other respiratory diseases. We aimed at evaluating the ability of clinical variables to predict HCT outcome in these patients. Methods: We studied all HCTs performed in our center between 2015 and 2019. Patients were grouped according to diagnosis: COPD, interstitial lung disease (ILD), cystic fibrosis (CF) and extra-pulmonary restrictive defect (EPR). Lung function tests and sea-level blood gases were used to predict HCT desaturation (ΔSpO2) and HCT outcome. The best predictors of ΔSpO2 and HCT outcome were analyzed using ROC analysis. The area under ROC were compared between predictors and the usual sea-level threshold of SpO2>95%. Results: 81 patients were included: 25 COPD, 23 ILD, 20 CF and 13 EPR. The variables most related to ΔSpO2 were residual volume (RV) for COPD (rho=0.68, p=0.02), sea-level PaCO2 for ILD (rho=-0.57, p=0.01), and RV and PaCO2 for EPR (rho=0.92, p=0.003 and rho=-0.64, p=0.03). No variables were significantly related to ΔSpO2 in CF patients. When predicting HCT outcome in COPD, ROC analysis for RV was not significant, but PaCO2 was predictive in ILD (AUROC=0.77, p=0.04) and EPR (AUROC=0.93, p=0.01, with cut-off value of PaCO2>43 mmHg showing a sensitivity and specificity of 80% and 100%). In ILD and EPR, the predictive value of PaCO2 was statistically higher than that of sea-level SpO2. Conclusion: In COPD and CF, no variables outperformed sea-level SpO2 to predict HCT outcome, while in ILD and EPR, sea-level PaCO2 was the best predictor. This should be taken into account when evaluating the need for an HCT in these populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".