Higher versus lower protein intake in formula-fed low birth weight infants
Bibliographic record
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: The aim of this review is to determine whether higher (>= 3.0 g/kg/day) versus lower (< 3.0 g/kg/day) protein intakes in formula fed preterm infants < 2.5 kilograms result in improved growth and neurodevelopmental outcomes without evidence of short and long‐term morbidity. Subgroup analyses will be undertaken to examine the following distinctions in protein intakes: a) low protein intake if the amount is less than 3.0 g/kg/day b) high protein intake if the amount is equal to or greater than 3.0 g/kg/day but less than 4.0 g/kg/day c) very high protein intake if the amount is equal to or greater than 4.0 g/kg/day If the literature combines alterations of protein and energy, subgroup analyses will be carried out for the planned categories of protein intake according to the following predefined non‐protein energy intake categories: a) Low energy intake, less than 105 kcal/kg/day b) Medium energy intake, greater than or equal to 105 kcal/kg/day and less than or equal to 135 kcal/kg/day c) High energy intake, greater than 135 kcal/kg/day Since the Ziegler‐Fomon reference fetus estimates different protein requirements for infants with differing birth weights, subgroup analyses will be undertaken for the following birth weight categories: a) < 800 grams b) 800 to 1199 grams c) 1200 to 1799 grams d) 1800 to 2499 grams
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".