Investigating the effectiveness of an intraoperative decision support guided fluid therapy intervention on postoperative outcome of high-risk patients undergoing high-risk abdominal surgery: protocol for an international multicentre stepped-wedge cluster-randomised implementation trial
Bibliographic record
Abstract
Background: Inappropriate fluid administration can impact patient outcome. Intraoperative advanced haemodynamic monitoring coupled with a treatment protocol based on stroke volume optimisation can help determine the appropriate timing for fluid boluses. Although recommended by several anaesthesia societies, this strategy is rarely implemented because protocols are complex and compliance is often poor. The Acumen Assisted Fluid Management (AFM) software is a decision support system that uses machine learning to predict fluid responsiveness and individualise fluid therapy. AFM reportedly predicts fluid responsiveness better than clinicians, decreases preload-dependent states, and improves both macro- and microcirculatory variables. The goal of this international multicentre stepped-wedge cluster randomised trial is to test whether implementing AFM during high-risk surgery improves patient outcome. Methods: The trial is ongoing in 16 academic hospitals in France, Belgium, Canada, and the USA. All centres (clusters) deliver routine care (control arm) at the start of the study and crossed over (one way) to AFM-guided fluid therapy (intervention arm). The time when different centres switch to AFM is randomised by an independent statistician. At the end of the trial, all centres will cross over to the intervention group. The primary outcome is a composite of major complications and death 30 days after surgery that will be analysed as intention-to-treat. A total of 2000 patients are required to detect a relative 20% differences in the primary outcome between groups. Conclusions: This trial is powered to provide evidence on whether implementing AFM is effective in reducing postoperative complications in high-risk patients after high-risk abdominal surgery. Clinical trial registration: NCT06011187.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".