Late Breaking Abstract - Automated oxygen administration vs manual oxygen therapy after major abdominal or thoracic surgery. An international multicenter randomized controlled study
Bibliographic record
Abstract
Background: Hypoxemia and hyperoxia may occur after surgery with related complications. Automated oxygen titration and weaning (FreeO2) may improve oxygenation and outcome. Aims and Objectives: To evaluate the impact of FreeO2 vs conventional O2 after surgeries at risk of desaturation, on the oxygenation parameters and patient’s outcome. Methods: This study is a prospective, multicenter, randomized, controlled, open trial. After thoracic or abdominal surgery, patients were randomly assigned to the manual O2 administration or automated O2 administration (FreeO2). Primary outcome was the percentage of time spent in the SpO2 target zone, during a 3-days time frame. Secondary outcomes were the nursing workload, the time spent with severe desaturation (SpO2<85%), with severe hyperoxemia (SpO2>98%); the O2 consumption, the duration of O2 administration during hospitalization, the frequency of use of ventilation (invasive or noninvasive) the hospitalization length of stay and the survival rate. Results: 200 patients were enrolled and 180 randomized and analyzed. There was no significant difference in baseline characteristics. Oxygenation and primary outcome are presented in the table: Conclusion: Automated oxygen titration and weaning significantly improves oxygenation parameters, decrease both severe hypoxemia and severe hyperoxemia after major horacic or abdominal surgery.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".