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Record W4404896603 · doi:10.14740/ijcp551

Accuracy of the Set Tidal Volume During Intraoperative Anesthetic Care: An <i>In Vitro</i> Evaluation

2024· article· en· W4404896603 on OpenAlexvenueno aff
Jennifer Sawyer, Kelly Moon, Michael Tobias, Julie Rice‐Weimer, Joseph D. Tobias

Bibliographic record

VenueInternational Journal of Clinical Pediatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnestheticVolume (thermodynamics)Set (abstract data type)AnesthesiaTidal volumeInternal medicineComputer scienceRespiratory system

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.442
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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