Bibliographic record
Abstract
Purpose: High gastric residual volumes (GRVs) are known to be one of the frequent causes of stopping enteral nutrition. This study was performed to investigate the gastric residual volume status in critically ill patients who were admitted to intensive care units. Methods: The subjects were 96 critically ill patients who were admitted to the ICU at ASAN Medical Center between October 1, 2008 and March 31, 2009. The measured volumes were categorized in 50 ml intervals from 0 to 500 ml. Results: Of the total GRVs measured, 46% were ＜50ml. The patients with a GRV ≥50 ml were 54% and 4% had a GRV ≥250 ml, whereas none of the patients` GRVs were ≥500 ml. When admitted to the hospital, There was a correlation between the APACHE 2 score and the gastric residual volume. This shows that the higher the APACHE2 score was the gastric residual volume. And there was a correlation between the APACHE 2 score and the loss of calories. This shows that the higher the APACHE 2 score was the loss of calories. Conclusion: The gastric residual volume of the critically ill patients under enteral nutrition in our hospital was not higher than that presented on the guidelines from the US and Canada. In addition, there was a big difference in the gastric residual volume among the critically ill patients depending on their clinical characteristics. Strict criteria for the gastric residual volume could be a factor for inhibiting proactive enteral nutrition. (KJPEN 2009;2(1):24-29)
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".