Measurement of long‐term iron absorption and loss during iron supplementation using a stable isotope of iron (<sup>57</sup>Fe)
Bibliographic record
Abstract
Summary We report the first measurements of long‐term iron absorption and loss during iron supplementation in African children using a stable isotope of iron (57Fe). After uniform labelling of body iron with 57Fe, iron absorption is proportional to the rate of decrease in the 57Fe tracer concentration, while iron loss is proportional to the rate of decrease in the 57Fe tracer amount. Anaemic Gambian toddlers were given 2 mg 57Fe orally to equilibrate with total body iron over 8–11 months. After assignment to the positive control arm of the HIGH study, 22 toddlers consumed a micronutrient powder containing 12 mg iron for 12 weeks followed by 12 weeks without iron supplementation. Their daily iron absorption increased 3·8‐fold during the iron supplementation period compared to the control period [median (interquartile range, IQR): 1·00 (0·82; 1·28) mg/day vs. 0·26 (0·22; 0·35) mg/day; P = 0·001]. Unexpectedly, during the supplementation period, daily iron loss also increased by 3·4‐fold [0·75 (0·55; 0·87) mg/day vs. 0·22 (0·19; 0·29) mg/day; P = 0·005]. Consequently, most (~72%) of the absorbed iron was lost during supplementation. Long‐term studies of iron absorption and loss are a promising and accurate method for assessing and quantifying long‐term iron balance and may provide a reference method for evaluating iron intervention programs in vulnerable population groups. This study was registered as ISRCTN 0720906.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".