Effect of excess Fluoride consumption on Urine-Serum Fluorides, Dental state and Thyroid Hormones among children in “Talab Sarai” Punjab Pakistan
Bibliographic record
Abstract
Abstract 190 children aged 7-18 years from an endemic fluorotic village “Talab Sarai (n = 130) and a non-fluorotic, control, village “Ottawa” (n = 60) were selected for comparison. Children were examined for fluoride (F-) concentration in drinking water, urine, and serum as well as Dental fluorosis (DF) and thyroid hormone levels. The mean concentration of water fluoride (WF) in the sample group was 6.23 mg/L, urine fluoride (UF) 3.38 mg/L, and serum fluoride (SF) 0.21 mg/L, while DF was 93.07%. Significant elevations (P = 0.000) in the concentration of all these four variables were observed in sample group children as compared to control. Mean Free Tetra-iodothyronine (FT4), Free Tri-iodothyronine (FT3) and Thyroid Stimulating Hormone (TSH) concentrations in the sample group were 16.64pmol/L, 5.57 pmol /L and 4.41 mlU/L, respectively. No marked difference in FT4 (P = 0.1) was noted, while significant elevations in FT3 and TSH (P = 0.000) were found in the sample relative to the control group. 80% of the children displayed clear thyroid hormonal derangements, with 36.92% having high TSH and 43.07% with FT3 and FT4 disorders. A moderate to strong correlation among WF, UF, SF and DF (r = 0.94, 0.60, 0.60, 0.72) and a very strong correlation between WF and TSH (r = 0.9) were observed. Our results suggest that excess F- level that is four times greater than the “safe limit” is not only increasing fluoride concentration in body fluids but is also affecting thyroid hormones in 4 out of 5 children which could lead to abnormal physical and mental growth in later developmental stages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".