A Comparative Analysis of Subacute Thyroiditis: Before and After COVID-19
Bibliographic record
Abstract
Background/Objective The COVID-19 infection impacts endocrine tissues and various thyroid disorders have been reported in affected patients. Subacute thyroiditis (SAT), a self-limited inflammatory thyroid condition, is associated with viral respiratory tract infections. Studies have suggested a link between COVID-19 and SAT. Our study expands on this by examining differences in SAT severity before and after COVID-19. Methods A retrospective cohort study analyzed SAT cases at an Ontario endocrinology clinic. Cases were identified as Pre-Covid (December 2015 - April 2019) or Post-Covid (December 2019 - April 2023). Patient charts were screened for diagnostic characteristics of SAT and relevant lab data, including TSH, FT4, FT3. From 436 Pre-Covid and 629 Post-Covid cases, 32 and 37 met inclusion criteria. Results Our study showed no difference between incidence of SAT cases Post-Covid versus Pre-Covid. However, Post-Covid, more patients had severe SAT, defined as values of FT4 and FT3 >30% above their upper normal range and TSH >10, (FT4 37.8 ∓ 6.7 vs. 50.9 ∓ 23.5 (p=0.05); FT3 9.6 ∓ 2.2 vs. 18.6 ∓ 10.9 (p=0.02)). No significant differences were identified in median TSH levels during hyperthyroid phase ( p =0.12). In the hypothyroid phase, median TSH values were significantly higher Post-Covid ( p =0.0046). Low FT4 values also showed significance ( p =0.001). Discussion Our study shows SAT incidence was similar pre and post COVID-19. Severity of the SAT disease course was amplified after the onset of the COVID-19 pandemic, with a significant elevation in FT4 and FT3 levels during hyperthyroidism and higher TSH during hypothyroidism in the Post-Covid cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".