Closed-Loop Fluid Resuscitation
Bibliographic record
Abstract
BACKGROUND: Surgical patients present with a wide variety of body sizes and blood volumes, have large differences in baseline volume status, and may exhibit significant differences in cardiac function. Any closed-loop fluid administration system must be robust against these differences. In the current study, we tested the stability and robustness of the closed-loop fluid administration system against the confounders of body size, starting volume status, and cardiac contractility using control engineering methodology. METHODS: Using an independently developed previously published hemodynamic simulation model that includes blood volumes and cardiac contractility, we ran a Monte-Carlo simulation series with variation in starting blood volume and body weight (phase 1, weight 35-100 kg), and starting blood volume and cardiac contractility (phase 2, contractility from 1500 [severe heart failure] to 6000 [hyperdynamic]). The performance of the controller in resuscitating to the target set point was evaluated in terms of milliliters of blood volume error from optimal, with <250 mL of error defined as "successful." RESULTS: One thousand simulations were run for each of the 2 phases of the study. The phase 1 mean blood volume error ± SD from optimal was 25 ± 59 mL. The phase 2 mean blood volume error from optimal was -60 ± 89 mL. The lower 95% Clopper-Pearson binomial confidence interval for resuscitation to within 250 mL of optimal blood volume for phase 1 and 2 was 99.6% and 97.1%, respectively. CONCLUSION: The results indicate that the controller is highly effective in targeting optimal blood and stroke volumes, regardless of weight, contractility or starting blood volume.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".