Effect of telemedicine support for intraoperative anaesthesia care on postoperative outcomes: the TECTONICS randomised clinical trial
Bibliographic record
Abstract
BACKGROUND: Telemedicine may help improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. METHODS: This was a single-centre randomised clinical trial of unselected adult surgical patients. Patients were randomised to receive usual care or decision support from a telemedicine service, which provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews and physiological alerts. ORs were randomised 1:1. The co-primary outcomes were 30-day all-cause mortality, respiratory failure, acute kidney injury, and delirium in the intensive care unit, analysed by intention to treat. RESULTS: Between July 1, 2019, and January 31, 2023, a total of 35,302 patients were randomised to receive telemedicine support, with 36,625 receiving usual care. Telemedicine clinicians provided review in 11,812/35,302 cases, with alerts delivered to 2044/35,302 patients. Telemedicine support had no effect on any of the co-primary outcomes. Within 30 days, 630/35,302 (1.8%) patients randomised to telemedicine died within 30 days, compared with 649/36,625 (1.8%) receiving usual care (relative risk [RR]1.01, 95% confidence interval [CI] 0.87-1.16, P=0.98). Telemedicine support did not alter postoperative respiratory failure [telemedicine 1071/33,996 (3.2%) vs usual care 1130/35,236 (3.2%), RR 0.98, 95% CI 0.88-1.09, P=0.98], acute kidney injury [telemedicine 2316/33 251 (7.0%) vs usual care 2432/34,441 (7.1%); RR 0.99, 95% CI 0.92-1.06, P=0.98], or delirium [telemedicine 1264/3873 (32.6%) vs usual care 1298/4044 (32.1%), RR 1.02, 95% CI 0.94-1.10, P=0.98]. CONCLUSIONS: In this large randomised clinical trial, intraoperative telemedicine decision support using real-time alerts and case reviews had no impact on adverse postoperative outcomes. CLINICAL TRIAL REGISTRATION: NCT03923699.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".