Preoperative skin antiseptics for preventing surgical site infections: what to do?
Bibliographic record
Abstract
BACKGROUND: Safe and effective patient preoperative skin antisepsis is recommended to prevent surgical site infections (SSIs), reduce patient morbidity, and reduce systemic costs. However, there is lack of consensus among best practice recommendations regarding the optimal skin antiseptic solution and method of application. METHODS: In 2010 and 2011 the health technology appraisal committee of the Surgery Operational Clinical Network (SOCN), of Alberta Health Services (AHS), conducted an environmental scan to determine the current preoperative skin antisepsis in Alberta, reviewed key publications and existing guidelines, and requested a systematic review from the Canadian Agency for Drugs and Technologies in Health (CADTH). Using this information, and an established protocol for evidence-informed recommendations, the health technology appraisal committee made recommendations that were, in 2012, reviewed and endorsed by the SOCN executive and the AHS-Infection Prevention and Control (IPC) group. RESULTS: The environmental scan revealed practice variation in the types of antiseptic solutions and application methods being used in the 18 Alberta hospitals surveyed. The systematic review suggested that preoperative antiseptic showering reduces skin flora but the effect on SSI rates was inconclusive. While the review found no conclusive evidence to recommend an optimal antiseptic solution or application method, the results of two large randomized controlled trials suggest that chlorhexidine in 70% alcohol is more effective than povidone iodine in the prevention of SSIs. These results and the recommendations from Safer Healthcare Now!, a program of the Canadian Patient Safety Institute (CPSI), were used to inform the recommendations for AHS. These recommendations included abandoning preoperative showering with antiseptics except for special cases (high-risk surgeries such as sternotomies and implants as recommended by IPC) and standardizing skin antiseptic application methods and solution to chlorhexidine (CHG) in 70% alcohol. The exception would be procedures involving the ear, eye, mouth, mucous membranes, neural tissue, infants and emergent trauma cases where povidine iodine should be used. CONCLUSION: Using the best available evidence it was recommended that AHS standardize surgical skin antisepsis to 2% CHG in 70% alcohol as the preferred antiseptic and povidone iodine, as an alternative when CHG is contraindicated, to reduce SSIs, practice variation, and health care costs. Further research is required to determine the optimal skin antiseptic solution to reduce SSIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".