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Record W4229585720 · doi:10.1213/ane.0b013e3182a96696

In Response

2013· letter· en· W4229585720 on OpenAlexaff
Anahi Perlas, Vincent Chan

Bibliographic record

VenueAnesthesia & Analgesia · 2013
Typeletter
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePulmonary aspirationGrading (engineering)Animal modelAnimal studiesHydrochloric acidSurgeryGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

We thank Drs. Bouvet and Chassard1 for their interest in our article.2 However, we respectfully disagree with their assumptions that our grading system is not clinically relevant and that any gastric volume over 0.8 mL/kg (50 mL for an average adult) poses a significant aspiration risk.1 In fact, the minimum gastric volume posing a risk of aspiration is currently controversial. Early extrapolations from animal models suggested thresholds of 0.4 mL/kg.3 More recently, Dr. Engelhardt et al.4 demonstrated that installation of 0.4 to 0.6 mL/kg of hydrochloric acid directly into monkeys’ tracheas causes clinical changes but not death and that the LD50 of hydrochloric acid instilled into the trachea of monkeys is 1.0 mL/kg. Based on these animal findings, they speculate that a volume of 0.8 mL/kg or approximately 50 mL may be a critical volume for severe aspiration.5 However, many experts have long questioned the validity of these extrapolations and they have suggested that arbitrary “thresholds” based on hydrochloric acid directly instilled into animals’ tracheas are not valid surrogates of gastric volumes and should be abandoned.5 Moreover, a plethora of evidence from clinical studies comprising more than 1000 patients shows that 0.8 mL/kg lies within the normal range of fasting gastric secretions and does not pose a significant aspiration risk.6–11 Human data consistently show that the mean gastric volume in fasted adults is 0.4 to 0.6 mL/kg and the upper limit of normal is approximately 1.5 mL/kg (or about 100 mL in the average adult).6–11 Therefore, a threshold of 0.8 mL/kg (corresponding to a supine antral area of 3.4 cm2) as suggested by Drs. Bouvet and Chassard would grossly overestimate aspiration risk in a large proportion of low-risk patients. Our own data from several studies is consistent with previous reports. In a prospective study (n = 200), 44% of fasted surgical patients presented a supine antral CSA >3.4 cm2.12 Similarly, 56% of fasted patients presenting for elective gastroscopy had a supine antral CSA >3.4 cm2, which again suggests this antral size is a normal finding.2 Therefore, although the threshold of gastric volume that increases aspiration risk is still debatable, clinical data strongly suggest it is much higher than that extrapolated from animal models, and likely greater than 1.5 mL/kg (approximately 100 mL for the average adult). Thus, a grading system than can differentiate volumes above and below this threshold is clinically relevant. This grading system, however, only gives a rough estimate of volume. To obtain a more precise estimate, we apply a simple mathematical model that is valid for a wide demographic range (nonpregnant adults with BMI up to 40).2 Having an accurate volume estimate, the clinician can then consider the clinical context of the individual patient, the presence of other risk factors, comorbidities, and the benefits and risks of alternate management strategies. Despite our disagreement as to what constitutes a “risky” threshold of gastric volume, we share Drs. Bouvet and Chassard’s enthusiasm for gastric sonography. As the first validated, noninvasive tool to assess the nature and volume of gastric content at the bedside, it is a great new resource for both researchers and clinicians. We are confident that this tool has now come of age, and it will soon demonstrate its full potential to help guide anesthetic management and prevent this most devastating perioperative complication.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.212
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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