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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".