Multicentre, Cluster‐Randomized Clinical Trial of Algorithms for Critical‐Care Enteral and Parenteral Therapy (ACCEPT).
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The provision of nutrition support for patients in intensive care units (ICUs) varies widely both within and between institutions. We tested the hypothesis that evidence‐based algorithms to improve nutrition support in the ICU would improve patient outcomes. Methods: A cluster‐randomized controlled trial was performed in the ICUs of 11 community and 3 teaching hospitals between October 1997 and September 1998. Hospital ICUs were stratified by hospital type and randomized to the intervention or control arm. Patients at least 16 years of age with an expected ICU stay of at least 48 hours were enrolled in the study ( n = 499). Evidence‐based recommendations were introduced in the 7 intervention hospitals by means of in‐service education sessions, reminders (local dietitian, posters) and academic detailing that stressed early institution of nutrition support, preferably enteral. Results: Two hospitals crossed over and were excluded from the primary analysis. Compared with the patients in the control hospitals ( n = 214), the patients in the intervention hospitals ( n = 248) received significantly more days of enteral nutrition (6.7 vs 5.4 per 10 patient‐days; p = .042), had a significantly shorter mean stay in hospital (25 vs 35 days; p = .003) and showed a trend toward reduced mortality (27% vs 37%; p = .058). The mean stay in the ICU did not differ between the control and intervention groups (10.9 vs 11.8 days; p = .7). Interpretation: Implementation of evidence‐based recommendations improved the provision of nutrition support and was associated with improved clinical outcomes.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it