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Record W2285828268

Reliability of ultrasound gastric volume assessment for the prevention of aspiration pneumonia

2013· article· en· W2285828268 on OpenAlexaff
Anahi Perlas, Liisa Davis, Cyrus Tse

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMedicineIntraclass correlationUltrasoundReliability (semiconductor)Aspiration pneumoniaAirwayInterim analysisAnesthesiaSurgeryNuclear medicinePneumoniaRandomized controlled trialRadiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

We have developed and validated a mathematical model (Volume = 27 + 14.6 x Right-lat CSA - 1.28 age) that accurately predicts gastric fluid volume based on a cross-sectional area of the gastric antrum obtained with 2D ultrasound in the right lateral decubitus position (Right-lat CSA). An accurate assessment of gastric volume can help tailor anesthetic or airway management to prevent aspiration penumonia in intensive care or anesthetic practice. We hypothesize that ultrasound gastric volume assessment has substantial intra-rater and inter-rater reliability (intraclass correlation coefficient > 0.6). We hereby present interim data (n=8) from a prospective, randomized, blinded study (final n=24). Three blinded raters perfomed bedside ultrasound assessment for determination of gastric fluid volume on subjects randomized to ingest 0 to 400 mL of apple juice in 100 mL intervals after an 8 hour fasting period. A standardized scanning protocol was used. The procedure was repeated after 24 hours on the same subjects for determination of intra-rater reliability. Preliminary results suggest there is sustantial intra-rater reliability (ICC = 0.75) and almost perfect inter-rater reliability (ICC = 0.84). In conlcusion, our preliminary results suggest that bedside sonographic assessment of gastric volume has substantial to almost perfect reliability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.015
GPT teacher head0.287
Teacher spread0.272 · 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
Published2013
Admission routes1
Has abstractyes

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