Trends in TSH, free T4, and anti-thyroid peroxidase and treatment status: Canadian Health Measures Survey 2012 to 2015
Bibliographic record
Abstract
Abstract Background Previous studies in Canada focused on the prevalence of thyroid conditions have not reported on the levels of the thyroid-stimulating hormone (TSH) and thyroid hormones. To address this issue, the present study describes the trends in TSH, free T4, and anti-thyroid peroxidase and their treatment status for the patients who have clinically high or low levels. Methods We used data from the Canadian Health Measures Survey (CHMS) cycles 3 and 4 conducted between 2012 and 2015. The thyroid measures studied were TSH, free T4, and anti-thyroid peroxidase. We used clinical reference ranges to identify abnormality in these measures. We labelled abnormality in these measures as treated if relevant conditions were diagnosed or a disease-specific prescription was reported. Untreated individuals were those with an abnormality in thyroid measures without any medication use or relevant diagnoses. We presented the trends of thyroid measures in mean values and ratios, compared to the values first measured. Results The levels of TSH, free T4, and anti-peroxidase in cycle 4 were not significantly different from those in cycle 3. The proportions of Canadians with clinically high levels of free T4, anti-thyroid peroxidase, and TSH were 0.03 to 0.017, 0.005 to 0.005, and 0.30 to 0.43 for cycles 3 to 4, respectively. The proportions of Canadians with clinically low levels of TSH and free T4 were 0.02 to 0.021 and 0.18 to 0.11 for cycles 3 to 4, respectively. The change in the proportions of treatment statuses varied across the thyroid measures of the Canadians studied. Conclusion This descriptive study demonstrates the trends in TSH, free T4, and anti-thyroid peroxidase; their distributions in the population; and the proportions of Canadians with clinically high or low levels. We believe the information on the treatment status of those with uncontrolled high levels can be used to design patient screening programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".