Youth of West Cameroon are at high risk of developing IDD due to low\ndietary iodine and high dietary thiocyanate
Bibliographic record
Abstract
Objectives: Hypothyroidism in utero leading to mental retardation is highly prevalent and recurrent in developing countries where iodine deficiency and thiocyanate overload are combined.So, to explore and identify human population's risks for developing iodine deficiency disorders and their endemicity in Western Cameroon, with the aim to prevent this deficiency and to fight again it, urinary iodine and thiocyanate levels were determined.Methods: The district of Bamougoum in Western Cameroon was selected for closer study due to its geographic location predisposing for iodine deficiency disorders (IDD).A comprehensive sampling strategy included 24-h urine samples collected over three days from 120 school-aged children.Urinary iodine and thiocyanate levels were measured by colorimetric methods.Results: Twenty one percent of boys between the ages 3 and 19 were classified as iodine deficient.The prevalence of thiocyanate overload in the same population was found to be 20%.Conclusion: Presence of endemic iodine deficiency and excessive thiocyanate in the population indicates that the region is at risk of iodine deficiency disorder.A multifactorial approach that includes improvement of diet, increasing iodine and minimizing goitrogen substances intake, soil and crop improvement and an iodine supplementation program may help alleviate IDD in the affected area studied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".