Venous Thromboembolism Following COVID-19 Vaccination in Patients With Hereditary Protein S Deficiency
Bibliographic record
Abstract
Hereditary protein S (PS) deficiency is a rare condition associated with increased risk of venous thromboembolism (VTE). In 2020, the coronavirus disease 2019 (COVID-19) pandemic prompted development of vaccinations to protect against the virus. PS deficiency is not a contraindication to COVID-19 vaccinations, but there are no studies regarding potential adverse effects in this population. We report two cases, a 43-year-old mother and her 18-year-old son, who developed VTE shortly after their first COVID-19 vaccines. Testing confirmed hereditary PS deficiency with a previously undescribed mutation in both cases. The temporal association between COVID-19 vaccination and VTE in these patients with hereditary PS deficiency suggests a potential causal relationship. However, it is unclear if this applies to all patients with hereditary PS deficiency. This highlights the importance of reporting adverse events following COVID-19 vaccinations in this population to evaluate the risks and benefits of vaccination.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".