Effect of increasing plant protein intake on protein quality and nutrient intake of US adults
Bibliographic record
Abstract
Three cycles of NHANES (2013–2018) were used to assess protein intake, protein quality (protein digestibility-corrected amino acid score; PDCAAS), protein cost, and nutrient intakes across quartiles of plant protein intake (Q1: <28.2%; Q2: 28.2% to <32.1%; Q3: 32.1 to <36.2%; Q4: ≥36.2%) over a 24 h period (≥19 years, n = 14 888). Grains represented the primary source of plant protein across quartiles and increased from 5.01% (Q1) to 13.5% (Q4). Across all the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS) food groups, grains were the most economical source of protein (24.3–26 g protein/$1). Legumes were the most economical plant-based protein (28–37.7 g protein/$1) within the “Protein Foods” FNDDS group. Absolute protein intake (96.6 g/day) and PDCAAS (0.91) of diets were progressively lower from Q1 to 72.0 g/day and 0.8 in Q4, respectively. Modelling the replacement of 50% of amino acids from grains in the diet with amino acids from legumes increased the PDCAAS by 10% in Q4. Intake levels of fibre, folate, iron, and zinc, were higher in Q2–Q4 compared to Q1 but had lower intakes of vitamins B12 and D. Amino acid complementation is required to effectively integrate higher levels plant protein into US diets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".