Iron release from the Lucky Iron Fish®: safety considerations.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The principal objective was to explore in greater detail safety issues with regard to the use of the Lucky Iron Fish® (fish) as a treatment for iron deficiency and iron deficiency anaemia in women in rural Cambodia. METHODS AND STUDY DESIGN: Experiments were done to determine: (1) purity of the iron in the fish by mass spectroscopy; (2) release of iron and contaminants released during boiling in water using inductive-ly-coupled plasma optical emission spectroscopy; (3) the impact of cooking time, acidity and number of fish in acidified water and two types of Khmer soups; and (4) drinkability of the water after boiling with different num-bers of fish. RESULTS: The fish is composed primarily of ferrous iron with less than 12% non-ferrous iron. Contaminants were either not detectable or levels were below the acceptable standards set by the World Health Organization. The length of time boiling the fish and the acidity of the water increased iron release but even with 5 fish boiled for 60 minutes, iron levels only approached levels where side effects are observed. Boiling one fish in water did not affect the perception of colour, smell or taste of the water but boiling in water with two or more fish resulted in the water being unpalatable which further limits the potential for iron toxicity from using the fish. CONCLUSIONS: The results suggest that the Lucky Iron Fish™ may be a safe treatment for iron deficiency.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".