Shoulder injury related to vaccine administration (SIRVA): What do we know about its incidence and impact?
Bibliographic record
Abstract
Background: Shoulder injury related to vaccine administration (SIRVA) has been recognised as the compensable term for any shoulder injury that may result from an improper vaccination technique since 2017, however, its incidence and impact remain poorly understood. Objectives: To examine knowledge of SIRVA through reported cases, determine SIRVA incidence related to COVID-19 vaccinations, and investigate recovery rates. Methods: Six pharmacovigilance agencies in the United States of America (USA), Canada, United Kingdom, European Union, Australia, and New Zealand were systematically search to identify all reported cases of SIRVA between January 2017 to July 2021. Primary outcome measures were SIRVA case reports. Secondary outcome measures included recovery status as well as vaccine received, age, and sex. SIRVA-related outcome measures were retrieved between July 18th and July 22nd 2021, with UK data received via personal correspondence. Results: Retrospective analysis yielded 505 SIRVA cases since 2017, with 330 (65%) of cases reported from January to July 2021. Sub-analysis, using COVID-19 data of 189 SIRVA cases from 891,906,986 vaccinations, estimated incidence to be 2 per 10 million. 32 cases (7%) had recovered from symptoms at the time of reporting, with 311 (62%) reported as 'not recovered', and 162 cases (32%) 'unknown'. Females represented 75% of reported cases. Conclusion: SIRVA case report numbers and incidence from COVID-19 data, compared with prior evidence, raises questions around health practitioner knowledge and reporting accuracy of SIRVA. Recovery rates are poorly understood. A global consensus definition of SIRVA and more transparent and routine reporting is required. The disproportionate representation of females is of concern with no known reasons for this disparity. Further research is needed on SIRVA knowledge in healthcare practitioners, reporting rates, incidence, management, and long-term outcomes for those impacted. Pharmacist vaccinators should be aware of their role in preventing SIRVA and be active in its detection.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".