Clinical Performance and Safety of Closed-Loop Systems: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Automated systems can improve the stability of controlled variables and reduce the workload in clinical practice without increasing the risks to patients. We conducted this review and meta-analysis to assess the clinical performance of closed-loop systems compared with manual control. Our primary outcome was the accuracy of closed-loop systems in comparison with manual control to maintain a given variable in a desired target range. The occurrence of overshoot and undershoot episodes was the secondary outcome. We retrieved randomized controlled trials on accuracy and safety of closed-loop systems versus manual control. Our primary outcome was the percentage of time during which the system was able to maintain a given variable (eg, bispectral index or oxygen saturation) in a desired range or the proportion of the target measurements that was within the required range. Our secondary outcome was the percentage of time or the number of episodes that the controlled variable was above or below the target range. The standardized mean difference and 95% confidence interval (CI) were calculated for continuous outcomes, whereas the odds ratio and 95% CI were estimated for dichotomous outcomes. Thirty-six trials were included. Compared with manual control, automated systems allowed better maintenance of the controlled variable in the anesthesia drug delivery setting (95% CI, 11.7%-23.1%; percentage of time, P < 0.0001, number of studies: n = 15), in patients with diabetes mellitus (95% CI, 11.5%-30.9%; percentage of time, P = 0.001, n = 8), and in patients mechanically ventilated (95% CI, 1.5%-23.1%; percentage of time, P = 0.03, n = 8). Heterogeneity among the studies was high (>75%). We observed a significant reduction of episodes of overshooting and undershooting when closed-loop systems were used. The use of automated systems can result in better control of a given target within a selected range. There was a decrease of overshooting or undershooting of a given target with closed-loop systems.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".