Automated O <sub>2</sub> Titration Alone or With High-Flow Nasal Cannula During Walking Exercise in Chronic Lung Diseases
Bibliographic record
Abstract
BACKGROUND: Exercise-induced O 2 desaturation contributes to dyspnea and exercise intolerance in various respiratory diseases. This study assessed whether automated O 2 titration was superior to fixed-flow O 2 to improve exertional dyspnea and walking exercise endurance. We also aimed at evaluating possible additive effects of high-flow nasal cannula coupled with automated O 2 titration on these outcomes. METHODS: Subjects with chronic respiratory diseases and exercise-induced desaturation performed a 3-min constant-speed shuttle test (CSST) and an endurance shuttle walking test (ESWT) with either (1) fixed-flow O 2 , (2) automated O 2 titration targeting an S pO 2 of 94% (± 2%), and (3) automated O 2 titration + high-flow nasal cannula according to a randomized sequence. The main outcome was Borg dyspnea score at the end of the 3-min CSST. Secondary outcomes included endurance time and dyspnea during ESWT and oxygenation status during exercise. RESULTS: Ten subjects with COPD, 10 with interstitial lung disease, 5 with pulmonary hypertension, and 3 with cystic fibrosis completed the study. Compared to fixed-flow O 2 , automated O 2 titration did not reduce dyspnea at the end of the 3-min CSST. Endurance time during the ESWT was prolonged with automated O 2 titration (mean difference 298 [95% CI 205–391] s, P < .001), and dyspnea at isotime was reduced. No further improvement was noted when high-flow nasal cannula was added to automated O 2 titration. Compared to fixed-flow O 2 , O 2 flows were higher with automated O 2 titration, resulting in better oxygenation. CONCLUSIONS: Automated O 2 titration was superior to fixed-flow O 2 to alleviate dyspnea and improve exercise endurance during the ESWT in subjects with a variety of chronic respiratory diseases. Adding high-flow nasal cannula to automated O 2 titration provided no further benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".