Effects of SARS-CoV-2 infection on hypothyroidism and subclinical hypothyroidism: a meta-analysis
Bibliographic record
Abstract
Background: In recent years, the outbreak of COVID-19 caused by SARS-CoV-2 has been witnessed globally. However, the impact of SARS-CoV-2 infection on thyroid dysfunction and subclinical thyroid dysfunction remains unclear. Therefore, this meta-analysis aimed to assess the effects of SARS-CoV-2 infection on thyroid dysfunction and its relationship with the severity of COVID-19. Methods: We systematically searched databases including PubMed, Willey Library, Embase, Web of Science, CNKI, Wanfang, and VIP. We focused on randomized controlled trials, case-control studies, and cohort studies published between December 2019 and August 2023, examining the association between SARS-CoV-2 infection and hypothyroidism, with a specific emphasis on the severity of the infection. The quality of the research was assessed using the Newcastle-Ottawa Scale (NOS), while statistical analysis was conducted using the meta and metafor packages in R 4.2.1 software. Results: ]. Additionally, two studies examining TT3 (a sample of 176 cases) and two studies examining TT4 (a sample of 176 cases) also showed no difference in TT3 and TT4 between the COVID-19 group and the control group, respectively. However, when evaluating the severity of COVID-19, six studies (565 samples) showed that TSH in the severe group was significantly lower than in the mild group [SMD = -0.55, 95% CI (-0.96, -0.14)], while FT3 was also lower in the severe group [SMD = -0.96, 95% CI (-1.24, -0.67)]. No noticeable differences were observed between the severe and mild groups in their TT3, FT4, and TT4 levels. Conclusion: SARS-CoV-2 infection may have detrimental effects on thyroid function in individuals with severe symptoms. More research is needed to confirm and explore this relationship. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023486042.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.051 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".