Clinical Implementation of Automated Oxygen Titration in a Tertiary Care Hospital
Bibliographic record
Abstract
BACKGROUND: When treating acute respiratory failure, both hypoxemia and hyperoxemia should be avoided. S pO 2 should be monitored closely and O 2 flows adjusted accordingly. Achieving this goal might be easier with automated O 2 titration compared with manual titration of fixed-flow O 2 . We evaluated the feasibility of using an automated O 2 titration device in subjects treated for acute hypoxemic respiratory failure in a tertiary care hospital. METHODS: Health-care workers received education and training about oxygen therapy, and were familiarized with an automated O 2 titration device (FreeO 2, ). A coordinator was available from 8:00 am to 5:00 pm during weekdays to provide technical assistance. The ability of the device to maintain S pO 2 within the prescribed therapeutic window was recorded. Basic clinical information was recorded. RESULTS: Subjects were enrolled from November 2020 to August 2022. We trained 508 health-care workers on the use of automated O 2 titration, which was finally used on 872 occasions in 763 subjects, distributed on the respiratory, COVID-19, and thoracic surgery wards, and in the emergency department. Clinical information could be retrieved for 609 subjects (80%) who were on the system for a median (interquartile range) of 3 (2-6) d, which represented 2,567 subject-days of clinical experience with the device. In the 82 subjects (14%) for whom this information was available, the system maintained S pO 2 within the prescribed targets 89% of the time. Ninety-six subjects experienced clinical deterioration as defined by the need to be transferred to the ICU and/or requirement of high flow nasal oxygen but none of these events were judged to be related to the O 2 device. CONCLUSIONS: Automated O 2 titration could be successfully implemented in hospitalized subjects with hypoxemic respiratory failure from various causes. This experience should foster further improvement of the device and recommendations for an optimized utilization.
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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".