Automatic<i>versus</i>manual oxygen administration in the emergency department
Bibliographic record
Abstract
Oxygen is commonly administered in hospitals, with poor adherence to treatment recommendations. We conducted a multicentre randomised controlled study in patients admitted to the emergency department requiring O2≥3 L·min−1. Patients were randomised to automated closed-loop or manual O2titration during 3 h. Patients were stratified according to arterial carbon dioxide tension (PaCO2) (hypoxaemicPaCO2≤45 mmHg; or hypercapnicPaCO2>45–≤55 mmHg) and study centre. Arterial oxygen saturation measured by pulse oximetry (SpO2) goals were 92–96% for hypoxaemic, or 88–92% for hypercapnic patients. Primary outcome was % time withinSpO2target. Secondary endpoints were hypoxaemia and hyperoxia prevalence, O2weaning, O2duration and hospital length of stay. 187 patients were randomised (93 automated, 94 manual) and baseline characteristics were similar between the groups. Time within theSpO2target was higher under automated titration (81±21%versus51±30%, p<0.001). Time with hypoxaemia (3±9%versus5±12%, p=0.04) and hyperoxia under O2(4±9%versus22±30%, p<0.001) were lower with automated titration. O2could be weaned at the end of the study in 14.1%versus4.3% patients in the automated and manual titration group, respectively (p<0.001). O2duration during the hospital stay was significantly reduced (5.6±5.4versus7.1±6.3 days, p=0.002). Automated O2titration in the emergency department improved oxygenation parameters and adherence to guidelines, with potential clinical benefits.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".