Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial
Bibliographic record
Abstract
Background: Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods: This single centre randomised clinical trial (RCT, clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between 2019-07-01 and 2023-01-31. Patients received usual-care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium in the Intensive Care Unit (ICU) were analysed as intention-to-treat. Results: The trial completed with 71927 surgeries (35302 ACT; 36625 usual care). The ACT performed 11812 case reviews and communicated alerts regarding 2044 intervention-group patients. There was no significant effect of the ACT vs. usual care on 30-day mortality [630/35302 (1.8%) vs 649/36625 (1.8%), RR 1.01 (95% CI 0.87 to 1.16), p=0.98], respiratory failure [1071/33996 (3.2%) vs 1130/35236 (3.2%), RR 0.98 (95% CI 0.88 to 1.09), p=0.98], AKI [2316/33251 (7.0%) vs 2432/34441 (7.1%), RR 0.99 (95% CI 0.92 to 1.06), p=0.98] or delirium [1264/3873 (32.6%) vs 1298/4044 (32.1%), RR 1.02 (95% CI 0.94 to 1.10), p=0.98]. There were no significant differences in secondary outcomes or sensitivity analyses. Conclusions: In this large RCT of intraoperative telemedicine decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest avenues where it may be more impactful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".