Ultrasound assessment of gastric volumes of thick fluids
Bibliographic record
Abstract
BACKGROUND: Enteral nutrition is essential in the treatment of critically ill patients. Current methods to monitor enteral nutrition such as aspiration of residual volume may be inaccurate. Gastric ultrasonography estimates total gastric fluid volume using the Perlas model, but this model is validated for clear fluids only, and its accuracy for measuring thick fluids is unknown. OBJECTIVES: The primary aim of this study was to evaluate the Perlas model for gastric volume estimation of enteral nutrition, a thick fluid product. DESIGN: A single-centre, single blinded, randomised controlled study. SETTING: Single university hospital, from May to July 2019. PARTICIPANTS: Seventy-two healthy fasted volunteers were randomly allocated to different fluid volume groups. INTERVENTION: Participants randomly ingested predetermined volumes between 50 and 400 ml of a feeding-drink (Nutricia Nutridrink). Following a standardised gastric ultrasound scanning protocol, a blinded sonographer measured the antral cross-sectional area in the supine and right-lateral decubitus positions. MAIN OUTCOME MEASURES: Measurements were performed at baseline, 5 min postingestion and 20 min postingestion. Gastric volumes were predicted using the previously established Perlas model and compared with total gastric fluid volumes after ingestion of the study drink. RESULTS: The Perlas model underestimated the volume of thick gastric fluid and yielded a suboptimal fit for our data. However, antral cross-sectional area and total gastric thick fluid volumes were significantly correlated (Pearson's correlation coefficient 0.73, P < 0.01). A new model was fitted to predict gastric volumes of thick fluids, using the antral cross-sectional area (cm2) in the right-lateral decubitus position: Volume (ml) = 79.38 + 13.32 x right-lateral cross-sectional area. CONCLUSION: The Perlas model for clear gastric fluid volume estimation is suboptimal for thick fluid volume assessment and an alternative model is presented. CLINICAL TRIAL REGISTRATION: Netherlands Trial Register Trial NL7677, Registration date: 16 April 2019; https://www.trialregister.nl/trial/7677.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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.
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".