Oxygen Conservation Methods With Automated Titration
Bibliographic record
Abstract
BACKGROUND: Oxygen titration is recommended to avoid hyperoxemia and hypoxemia. Automated titration, as well as the [Formula: see text] target, may have an impact on oxygen utilization, with potential logistical effects in emergency and military transportation. We sought to assess the oxygen flow required for different [Formula: see text] targets in spontaneously breathing subjects, and to evaluate individualized automated oxygen titration to maintain stable oxygenation in subjects with COPD and healthy subjects with induced hypoxemia. METHODS: In the first part of the study, oxygen flow was evaluated in hospitalized subjects for different [Formula: see text] targets from 90% to 98%. Oxygen requirements to reach these targets were determined using a device that automatically adjusts oxygen flow every second on the basis of the [Formula: see text] target. In the second part of the study, the same automated oxygen titration method was used to correct hypoxemia in subjects with COPD and in healthy subjects with induced hypoxemia while the subjects wore a gas mask. Oxygen flow, [Formula: see text], and heart rate were continuously recorded. RESULTS: Thirty-six spontaneously breathing hospitalized subjects were included in the first part of the study. Oxygen flow was reduced more than 6-fold when the [Formula: see text] target was decreased from 98% to 90%. The second part of the study included 15 healthy and 9 subjects with stable COPD. In healthy subjects, heterogeneous oxygen flows were required to correct induced hypoxemia (0.2-2.5 L/min). In subjects with COPD, oxygen flow varied from 0 L/min (in 9 of 18 tested conditions) to 2.9 L/min. CONCLUSIONS: Significant reductions in the amount of oxygen delivered could be obtained with optimized [Formula: see text] targets. Oxygen delivery through a gas mask to correct hypoxemia is feasible, and automated oxygen titration may help individualize oxygen administration and reduce oxygen utilization. (ClinicalTrials.gov registration: NCT02782936, NCT02809807.).
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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) | 0.000 | 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 | 0.000 |
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".