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Record W2259597047

Preoperative skin antiseptics for preventing surgical site infections: what to do?

2014· article· en· W2259597047 on OpenAlexaboutno aff
Paule Poulin, Kelly Chapman, Lynda McGahan, Lea Austen, Trevor Schuler

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAntisepticChlorhexidineInfection controlRandomized controlled trialIntensive care medicineSystematic reviewHealth careCritical appraisalSurgeryMEDLINEAlternative medicineDentistry
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.090
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0050.004
Science and technology studies0.0020.004
Scholarly communication0.0080.008
Open science0.0030.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.019
GPT teacher head0.285
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

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