Thyroid Gland Dysfunction and COVID-19 Severity: Is There a Correlation?
Bibliographic record
Abstract
Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus pandemic to break out in 2019 has resulted in a serious health disaster all over the world. It has been suggested that thyroid gland dysfunction in coronavirus disease 2019 (COVID-19) patients can directly or indirectly affect COVID-19 severity and mortality rates. The aim of this study was to examine the possible effect of thyroid dysfunction on the severity and mortality in COVID-19 hospitalized patients. Methods: This is a retrospective study that included 415 COVID-19 patients, who were admitted to the Ziv Medical Center between April 2020 and October 2021. Clinical and demographic data were collected from patient’s electronic medical records. Results: Patients with severe COVID-19 disease hospitalized in COVID-19 intensive care unit (ICU) department had significantly lower average level of triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH) levels compared with patients with mild or moderate disease in hospitalized patients in COVID-19 ward. Regarding hospitalization length, there was a statistically significant difference between the two groups with a mean of 14.6 days for COVID-19 ICU department and 8.6 days for the COVID-19 ward (P < 0.001). Similarly, the mortality rate also showed a significant difference, with 48.8% in COVID-19 ICU department compared to 10.4% in the COVID-19 ward (P < 0.001). However, it is worth noting that ethnicity, gender, body mass index (BMI), and chronic diseases did not exhibit any significant differences between the two groups. In a multivariate logistic regression model the risk of mortality was three times higher among patients with lowered T4 levels and six times higher among patients with lower TSH levels. Longer duration of hospitalization (odds ratio (OR) = 1.12) and older age (OR = 1.07) were also associated with higher mortality, while T3 was inversely associated (OR = 0.07). Conclusions: It was suggested that lower thyroid hormones and TSH serum levels were associated with increased severity and mortality in COVID-19 hospitalized patients. Thyroid hormones and TSH levels may serve as additional tools for better evaluation of COVID-19 severity and mortality. J Endocrinol Metab. 2023;13(3):96-103 doi: https://doi.org/10.14740/jem883
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".