Low phytic acid pea supplementation as an approach to combating iron deficiency in female runners: A randomized control trial
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: Iron deficiency (ID) is the most prevalent micronutrient deficiency in the world and the leading cause of anemia globally. Female athletes are at a disproportionate risk for ID due to blood loss through menstruation and decreased iron absorption secondary to exercise. Field peas are a rich source of iron but, similar to iron from other plant-based sources, the iron has limited bioavailability due to high levels of phytic acid, an inherent compound that binds to cations, creating a salt (phytate), which limits absorption during digestion. Aim: The purpose of our research was to investigate the effect of a field pea variety bred to have low levels of phytic acid on plasma ferritin, exercise performance, and body composition in female runners. Methods: Twenty-eight female runners (age:34.6 ± 9.7 years; weight: 65.1 ± 8.1 kg; VO 2 max: 50.7 ± 8.9 ml/kg/min) underwent measures of ferritin, exercise performance, and body composition before and after being randomly assigned to consume a powder derived from regular peas, low phytic acid peas, or a non-pea control (maltodextrin), plus vitamin C for 8 weeks. Results: The regular pea and low phytic acid pea groups had a 14.4% and 5.1% increase in plasma ferritin, respectively, while the maltodextrin group had a decrease of 2.2%; however, the difference in changes between groups was not statistically significant. No differences between groups were evident in any of the other measures. Conclusion: Larger doses or longer duration of pea supplementation may be necessary to induce meaningful changes in iron status. This trial was registered at ClinicalTrials.gov (NCT04872140).
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it