Automated Oxygen Flow Titration to Maintain Constant Oxygenation
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: One century after the introduction of the oxygen flow meter into clinical practice, we have developed a device, FreeO(2), that automatically titrates the oxygen flow delivered to spontaneously breathing patients, with the aim of maintaining a stable S(pO(2)). We evaluated this system in healthy subjects during induced hypoxemia. METHODS: Hypoxemia was induced in 10 healthy subjects while breathing a gas mixture of variable F(IO(2)) (air + nitrogen). Each subject performed 3 hypoxemic challenges with the addition, in a random order, of either: air with constant flow (1.5 L/min); oxygen with constant flow (1.5 L/min); or automatic oxygen flow titration. Subjects were blinded to the intervention. Oxygen flow, S(pO(2)), end-tidal CO(2), respiratory rate, and heart rate were recorded every second. The primary outcome was the time with S(pO(2)) between 92% and 96%. RESULTS: The S(pO(2)) target (92-96%) was achieved a median of 26.0%, 36.8%, and 66.5% (P < .001) of the time with air, constant oxygen, and automated oxygen titration, respectively. Severe oxygen desaturations (S(pO(2)) < 88%) were respectively observed at a median of 33.7%, 12.7%, and 0.4% of the time (P < .001). Hyperoxia was present a median of 4.1%, 39.1%, and 14.5% of the time (P < .001). Tachycardia was present with air and with constant oxygen flow, but not while using automated oxygen titration. These results were obtained with a mean and maximal oxygen flow of 1.3 L/min and 7.6 L/min with the automated titration. CONCLUSIONS: In this model of induced hypoxemia, the FreeO(2) system that automatically titrates the oxygen flow was more efficient at maintaining the S(pO(2)) target, while ensuring a statistically significant reduction in the rates of severe hypoxemia and hyperoxia, in comparison with air or constant oxygen flow. These beneficial results were obtained with less oxygen, in comparison to a constant oxygen flow.
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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) | 0.000 | 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) | 0.000 | 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 it