Impact of automatic oxygen titration alone or with high flows on exercise tolerance in patients with lung disease and exercise oxygen desaturation
Bibliographic record
Abstract
Introduction: This study assesses the effects of automated oxygen titration, alone or with high flows on dyspnea and exercise tolerance in patients with chronic lung disease and exercise-induced O2 desaturation. Methods: Patients with chronic lung diseases and exercise desaturation were involved in a 3 treatment arm cross-over study to perform a 3-min constant speed walk test (3min CSST) and an endurance shuttle walking test (ESWT) carried out with one of the 3 oxygen delivery systems: (1) O2 at fixed-flow of 2 L/min (2) or with an automated O2 titration system (FreeO2®) targeting 94% SpO2, and (3) FreeO2 in + high flow nasal cannula (FreeO2 + Airvo®). The main outcome was the dyspnea score (modified Borg scale) following 3min CSST. Secondary outcomes were endurance time and mean/nadir SpO2 during ESWT. Results: In this interim analysis, 14 patients (7 COPD and 4 ILD, 2 PH and 1 CF) were included. There was no difference in the dyspnea score. Endurance time was longer (p<0.001) while mean and nadir SpO2 were higher (p<0.001) with FreeO2 vs fixed-flow O2. There was no further benefit of adding AirvoTM to FreeO2 on endurance time or SpO2. Conclusion: These preliminary results suggest that, despite no improvement in dyspnea, automatic O2 titration increases endurance time and SpO2 during ESWT compared to fixed-flow O2. Adding AirvoTM had 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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".