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 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".