Automated closed-loop<i>versus</i>standard manual oxygen administration after major abdominal or thoracic surgery: an international multicentre randomised controlled study
Bibliographic record
Abstract
INTRODUCTION: Hypoxaemia and hyperoxaemia may occur after surgery, with related complications. This multicentre randomised trial evaluated the impact of automated closed-loop oxygen administration after high-risk abdominal or thoracic surgeries in terms of optimising the oxygen saturation measured by pulse oximetry time within target range. METHODS: After extubation, patients with an intermediate to high risk of post-operative pulmonary complications were randomised to "standard" or "automated" closed-loop oxygen administration. The primary outcome was the percentage of time within the oxygenation range, during a 3-day frame. The secondary outcomes were the time with hypoxaemia and hyperoxaemia under oxygen. RESULTS: 177.9±277.2 min (27.0±23.8%), difference -22.0% (95% CI -27.6--16.4%); p<0.0001). Kaplan-Meier analysis depicted a significant difference in terms of hypoxaemia (p=0.01) and severe hypoxaemia (p=0.0003) occurrence between groups in favour of the automated group. 25 patients experienced hypoxaemia for >10% of the entire monitoring time during the 3 days within the standard group, as compared to the automated group (p<0.0001). CONCLUSION: Automated closed-loop oxygen administration promotes greater time within the oxygenation target, as compared to standard manual administration, thus reducing the occurrence of hypoxaemia and hyperoxaemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".