New onset autoimmune disease following a SARS-CoV-2 infection: A systematic review protocol
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of people worldwide. While most infected individuals who survive do so with no long-term consequences, approximately 10 to 70% develop long-term sequelae. Of particular concern has been the development of autoimmune diseases. Viral triggers for autoimmune disease have been thoroughly studied for previous viral infections and several recent studies have sought to investigate the link between SARS-CoV-2 and new onset autoimmune disease. Several reviews have also been conducted on the topic, however, many of these reviews are limited in focus, emphasizing biological mechanisms and case reports, as opposed to estimates of risk. Further, these reviews do not capture more recent cohort studies that have been published investigating the association between SARS-CoV-2 and new onset autoimmune disease. Therefore, there is a need for a more comprehensive and temporally updated systematically conducted review of the literature to address the question What is the risk of incident (i.e., new onset) autoimmune disease following a SARS-CoV-2 infection among adults (≥18 years)?. A systematic search of MEDLINE, EMBASE, CINAHL, and grey literature will be conducted, with results screened in duplicate in two stages: 1) Title and abstract screening and 2) Full text screening. A standardized data extraction sheet will be used on any studies passing through both stages of screening to extract details on publication, study population, exposure, and outcomes. Narrative and tabular synthesis of overall findings will be conducted, with diversity and heterogeneity of included studies discussed. If possible, a meta-analysis will also be conducted to combine findings of risk across the included studies. This protocol has been registered to PROSPERO (registration number: CRD42024594446).
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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 | 0.048 | 0.052 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.007 |
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".