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Record W2117242160 · doi:10.1079/phn2005830

Excess dietary iodine intake in long-term African refugees

2006· article· en· W2117242160 on OpenAlexfundno aff
Andrew Seal, Paul I. Creeke, D. Gnat, Fathia Abdalla, Zahra Mirghani

Bibliographic record

VenuePublic Health Nutrition · 2006
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsIodineMedicineIodine deficiencyEnvironmental healthRefugeeIodised saltExcretionAnimal scienceThyroidEndocrinologyChemistryGeographyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the iodine status of long-term refugees dependent on international food aid and humanitarian assistance. DESIGN: A series of cross-sectional two-stage cluster or systematic random sample surveys which assessed urinary iodine excretion and the prevalence of visible goitre. Salt samples were also collected and tested for iodine content by titration. SETTING: Six refugee camps in East, North and Southern Africa. SUBJECTS: Male and female adolescents aged 10-19 years. MAIN RESULTS: The median urinary iodine concentration (UIC) ranged from 254 to 1200 microg l(-1) and in five of the camps exceeded the recommended maximum limit of 300 microg l(-1), indicating excessive iodine intake. Visible goitre was assessed in four surveys where it ranged from 0.0 to 7.1%. The camp with the highest UIC also had the highest prevalence of visible goitre. The iodine concentrations in 11 salt samples from three camps were measured by titration and six of these exceeded the production-level concentration of 20 to 40 ppm recommended by the World Health Organization (WHO), but were all less than 100 ppm. CONCLUSIONS: Excessive consumption of iodine is occurring in most of the surveyed populations. Urgent revision of the level of salt iodisation is required to meet current WHO recommendations. However, the full cause of excessive iodine excretion remains unknown and further investigation is required urgently to identify the cause, assess any health impact and identify remedial action.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.321
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2006
Admission routes1
Has abstractyes

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