Accuracy of the Set Tidal Volume During Intraoperative Anesthetic Care: An <i>In Vitro</i> Evaluation
Bibliographic record
Abstract
Background: Precise adjustment of tidal volume (Vt) and minute ventilation remains a key component of intraoperative care. Control of Vt is regulated by internal pneumotachometers and flow meters, which may be separated from the patient by the anesthesia circuit and the internal circuitry of the anesthesia machine. Given this arrangement, there may be variations in delivered and exhaled (measured) Vt depending on the site of measurement. Methods: The current study used an in vitro model to determine variations in inspired and expired Vt during various volume- and pressure-controlled modes of ventilation using an Avance CS 2 anesthesia machine. Results: During in vitro mechanical ventilation using pressure limited (15 and 20 cm H 2 O) and volume limited (Vt 50, 100, 200, and 300 mL), we saw slight discrepancies between Vt measured using a pneumotachometer placed between the endotracheal tube (ETT) and the anesthesia circuit as compared to those measured internally by the anesthesia machine. Conclusions: Although the differences were statistically significant, the variations were no more than 5-6% at most at the higher Vt with either volume- or pressure-limited ventilation. These differences are unlikely to be clinically significant, thereby demonstrating the accuracy and safety of anesthesia machines from the modern era. Int J Clin Pediatr. 2024;13(3):69-72 doi: https://doi.org/10.14740/ijcp551
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".