Autoimmune manifestations following COVID-19 infection in two individuals with primary immunodeficiency
Bibliographic record
Abstract
Background: Due to widespread vaccination efforts worldwide, the mortality rates linked to COVID-19 have been decreasing. Nevertheless, there persists a notable level of morbidity, marked by increased occurrences of post-COVID-19 conditions. This includes the development of new autoimmune and inflammatory diseases in individuals who have recovered from COVID-19. A more severe progression of COVID-19 has been correlated with an increased probability of newly diagnosed autoimmune disease, and among individuals with pre-existing autoimmune conditions, COVID-19 increased the risk of developing another autoimmune disease. Methods: Our patients’ medical records were analyzed retrospectively, including their medical history. Results: We present two cases of primary immunodeficiency patients. One of them experienced the onset of new autoimmune symptoms, while the other had a worsening of her autoimmune condition following COVID-19 infection. Conclusion: Recognizing the potential connection between COVID-19 and autoimmune conditions is crucial for identifying symptoms promptly in primary immunodeficiency patients and ensuring timely treatment. Further research is required to comprehensively grasp the relationship between COVID-19 and the development of autoimmunity in this particular patient group. Statement of novelty: In this paper, we present a novel exploration into the emergence of autoimmune manifestations in primary immunodeficiency patients subsequent to COVID-19 infection, through an analysis of two distinct case reports.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".