Eliminate the In Vivo Digestibility Requirement for Protein Content Claims in North America to Align Consumer Purchasing Behavior with Dietary Guidelines
Bibliographic record
Abstract
A roundtable discussion, held on 10 December, 2024, addressed requirements for protein quality assessment in United States and Canadian food labeling regulations, focusing on concerns with the protein digestibility-corrected amino acid score (PDCAAS), which includes an in vivo rat assay to determine true fecal protein digestibility. Because animal proteins tend to score higher, the PDCAAS disadvantages nonanimal foods in substantiating protein content claims despite dietary guidelines recommending increased intake of plant proteins. In addition, the use of animal testing raises ethical concerns for some consumers. Roundtable participants weighed the benefits and costs of requiring the PDCAAS and discussed alternative regulatory approaches to better promote human health, prevent chronic disease, replace animal testing, and support sustainable food production. Options included relying solely on the amount of protein per serving, correcting only for the amino acid score, using fixed coefficients of digestibility or in vitro assays to determine digestibility, and incorporating measures that reflect human health outcomes and environmental impact. Several in vitro methods, such as the pH-drop and pH-stat methods, were identified as promising candidates for regulatory acceptance. The consensus was that for foods that do not address special needs, relying solely on the amount of protein to substantiate content claims is appropriate for populations who already consume protein in excess of reference values from varied sources. This approach, already used in other high-income jurisdictions, allows more plant-based foods to qualify for protein claims while avoiding animal testing. Moving away from the in vivo derived PDCAAS would reduce existing regulatory barriers, better align with current dietary guidelines, and promote increased intake of plant-based foods, thereby improving public health and sustainability.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".