Effect of alcohol skin cleansing on vaccination-associated infections and local skin reactions: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Recommendations regarding the need to use alcohol prior to vaccine injections are inconsistent and based on low-level evidence. The objective was to assess the effectiveness of alcohol in reducing local skin reactions and infection post-vaccination. METHODS: Randomized controlled trial in a pediatric clinic. A research assistant cleansed the skin with alcohol at (swab group) or adjacent to (control group) the pre-defined injection site(s). Clinicians, parents and children were blinded to group allocation. Parents reported local skin reactions using paper diaries for 15 days post-vaccination (Day 0-14). Telephone interviews were conducted Day 1, 5, and 14. The Brighton Collaboration criteria were used to diagnose cellulitis and infectious abscess Day 5 and afterward. RESULTS: 170 children participated (May-November 2017). Baseline characteristics did not differ (p > 0.05) between groups. Children received 1-4 separate injections. There were no differences between swab and control groups in the incidence of any local skin reactions (58% vs. 54%), and specifically, pain (45% vs. 40%), redness (26% vs. 21%), swelling (20% vs. 13%), warmth (19% vs. 27%), and spontaneous drainage of pus (0% in both groups) over the post-vaccination follow-up period. Day 5 data was available for 99% of participants from diaries and telephone surveys; there were no cases of cellulitis or infectious abscess. CONCLUSION: These findings are the first direct evidence for vaccine injections demonstrating that cleansing the skin with alcohol may not be needed. Our study is underpowered; however, to detect a difference in incidence of skin infection, future research is recommended.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".