Computer-aided support improves early and adequate delivery of nutrients in the ICU.
Bibliographic record
Abstract
BACKGROUND: In 2007 a national guideline on perioperative nutrition was issued in the Netherlands. As external indicator for adequacy of nutritional therapy, the percentage of malnourished patients who reach at least 1.2 grams of protein on day 4 after admission was chosen by the Netherlands Health Care Inspectorate. METHODS: We developed an algorithm that allows users to ask for advice on which artificial nutritional formula to prescribe and at which rate, assuring provision of adequate amounts of both protein and energy. Feedback on nutritional therapy is given to the users on a daily basis, and to the management per quarter. Both the advice and the feedback have been integrated in our data management system. The advice module is also available on-line. RESULTS: In the baseline situation over the first four quarters (2006) an average of 30.2% of patients who had a full day 4 in our unit reached the protein indicator. In the last six quarters post-implementation, the average percentage reached was 56.5% with values consistently over 50%. Changes were statistically significant at third quarter of 2007 (p<0.05) and thereafter (p<0.001). Results for day 7 of admission were unaffected, which indicates that targets were reached earlier during hospital stay. CONCLUSION: Our study shows that integration of nutritional advice and automatically generated feedback to users in a data management system consistently improves delivery of (early) nutrition.
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 | 0.001 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".