Omission of alcohol skin cleansing and risk of adverse events in long-term care residents undergoing COVID-19 vaccination: A cohort study
Bibliographic record
Abstract
Despite a lack of clinical data demonstrating the effectiveness of alcohol swab cleansing prior to vaccinations as a prophylactic measure to prevent skin infections, it is recommended for vaccine administration by the Canadian Immunization Guide. The objective of this study was to evaluate the risk of adverse events after omitting alcohol skin cleansing in long-term care (LTC) residents receiving vaccinations during the COVID-19 pandemic. Two medium-sized LTC homes participated in a cohort study, whereby one LTC used alcohol swab cleansing prior to resident vaccinations and the other did not. All residents received two doses of the BNT162b2 COVID-19 vaccine separated by an average (SD) 29.3 (8.5) days. The electronic chart records of participants were reviewed by researchers blinded to group allocation to assess for the presence of adverse events following immunization (AEFI), including reactogenicity, cellulitis, abscess, or systemic reactions. Log-binomial regression was used to compute risk ratios (with 95% confidence intervals) of an AEFI according to alcohol swab status. 189 residents were included, with a total of 56 AEFI between the two doses. The risk of reactogenicity (adjusted RR 0.54, 95% CI 0.17-1.73) or systemic reactions (adjusted RR 0.75, 95% CI 0.26-2.13) did not differ for the residents that received alcohol skin antisepsis compared to those that did not. There were no cases of cellulitis or abscess. This study did not demonstrate an elevated risk of AEFI in LTC residents receiving two doses of the BNT162b2 mRNA COVID vaccine without alcohol skin antisepsis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".