Automated Oxygen Flow Titration to Maintain Constant Oxygenation
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".