HYPERTHYROIDISM
Bibliographic record
Abstract
Thyroid disorders are one of the common endocrine disorders. Their prevalenceis affected by many factors, especially environmental and nutritional ones. Objectives: Toascertain the seroprevalence of hyperthyroidism in clinically suspected hyperthyroid patients.Design: Descriptive cross-sectional study. Setting: Frontier Medical & Dental College,Abbottabad. Period: January to August, 2015. Methods: One hundred and twenty patientswere included in the study based on inclusion and exclusion criteria. Thyroid function tests(TSH, fT3 & fT4) were performed using enzyme linked immunoassay (ELISA) method. Results:There was preponderance of males in our study with male to female ratio of 1.4:1. Maximumpatients (56.67 %) were between the ages of 21-40 years of age, with mean age of studypopulation to be 32.09±13.01 years. The prevalence of hyperthyroidism was 15% based on theresults of thyroid function tests. There were 11 males and 7 females with male to female ratio of1.57:1 and mean age of hyperthyroid patients was 25.72± 8.27 years. The mean value of TSH,fT3 and fT4 was 0.038±0.025 mIU/L, 7.08±2.19 ng/mL and 25.25±6.30 μg/dL respectively inhyperthyroid patients as compared to 3.15±2.23 mIU/L, 2.05±0.88 ng/mL and 10.69±2.69μg/dL in euthyroid subjects. Conclusion: Thyroid disorders are not very rare among generalpopulation. TFTs provide a reliable way of ascertaining the thyroid function. As this is a hospitalbased study, field studies should be conducted to ascertain the true prevalence of thyroiddisorders in the community.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".