Endocrine disorders following SARS-CoV-2 vaccination: a comprehensive systematic review
Bibliographic record
Abstract
Purpose: To synthesize and analyze the current available evidence on the development of new-onset endocrine disorders associated with coronavirus disease 2019 (COVID-19) vaccination. Materials and Methods: We performed a systematic review of literature by searching PubMed, Scopus, Embase and Web of Science. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were applied, the Joanna Briggs Institute tool and New Castle-Ottawa score were used to assess the risk of bias and quality. SPSS 25.0 software was used for statistical analysis. Results: A total of 245 patients were reported from the selected studies. The most frequently reported endocrine disorders associated with COVID-19 vaccination were thyroid conditions (70.6%), primarily subacute thyroiditis (69.9%) and Graves' disease (28.9%). Cases of type 1 diabetes mellitus (10.2%), adrenal disorders (9.8%), and pituitary disorders (9.4%) were also identified. Most cases occurred in women (64%) and following the first vaccine dose (50.6%). Messenger ribonucleic acid-based vaccines primarily Pfizer-BioNTech and Moderna were the most reported (56.7%). Additionally, studies on fertility found no significant adverse effects on ovarian reserve or semen quality. Clinical outcomes were favorable in most cases (82%), with no significant mortality reported. Conclusion: Overall, although cases of endocrine disorders following COVID-19 vaccination have been reported, a causal relationship has not been definitively established. The benefits of vaccination significantly outweigh the potential risks of endocrinological complications at both the individual and population levels. Nevertheless, clinicians should remain alert to the possibility of endocrine manifestations following vaccination. Trial Registration: PROSPERO identifier: CRD42024512710.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".