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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".