Postoperative Remote Automated Monitoring: Need for and State of the Science
Bibliographic record
Abstract
Worldwide, more than 230 million adults have major noncardiac surgery each year.Although surgery can improve quality and duration of life, it can also precipitate major complications.Moreover, a substantial proportion of deaths occur after discharge.Current systems for monitoring patients postoperatively, on surgical wards and after transition to home, are inadequate.On the surgical ward, vital signs evaluation usually occurs only every 4-8 hours.Reduced in-hospital ward monitoring, followed by no vital signs monitoring at home, R ESUM EChaque ann ee, plus de 230 millions d'adultes à travers le monde subissent une chirurgie non cardiaque majeure.Si les interventions chirurgicales peuvent am eliorer la qualit e et prolonger la dur ee de la vie, elles peuvent aussi pr ecipiter l'apparition de complications majeures.De plus, une proportion appr eciable des d ecès se produit après la sortie de l'hôpital.Les systèmes actuels de surveillance des patients après l'op eration, dans les services de chirurgie et après leur retour à la maison sont insuffisants.Lorsque le patient est hospitalis eWorldwide, more than 230 million adults (>500,000 Canadians) have major noncardiac surgery annually.Among those having surgery, average age and comorbidities are rising.1 Although surgery can improve patient outcomes, it can also precipitate major complications. 1 More than 10% of surgical patients, age 45 and older, will suffer a major postoperative
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".