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Record W2904975162 · doi:10.1017/ice.2018.303

Infection prevention in the operating room anesthesia work area

2018· article· en· W2904975162 on OpenAlexaff
L. Silvia Muñoz-Price, Andrew Bowdle, Bree Johnston, Gonzalo Bearman, Bernard Camins, E. Patchen Dellinger, Marjorie Geisz-Everson, Galit Holzmann-Pazgal, Rekha Murthy, David A. Pegues, Richard C. Prielipp, Zachary Rubin, Joshua K. Schaffzin, Deborah S. Yokoe, David J. Birnbach

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsDalhousie University
FundersAmerican Association of Nurse AnesthetistsChildren's Hospital ColoradoSyracuse UniversityUnited States Pharmacopeia
KeywordsContent (measure theory)Work (physics)Action (physics)MedicineComputer scienceAnesthesiaMultimediaEngineeringMathematics

Abstract

fetched live from OpenAlex

PurposeThe potential for clinically significant microbial cross transmission in the intraoperative environment poses a threat to patient safety.A growing body of literature has shown contamination in the anesthesia work area, including the anesthesia medical work cart, stopcocks, laryngeal masks and laryngoscope blades, touchscreens, and keyboards, as well as on providers' hands, resulting in transmissions, healthcare-associated infections, and increased risk of patient mortality.The authors acknowledge that the operating room (OR) is a challenging environment in which to affect ideal infection prevention and control practices.In addition, infection prevention and control policies specific to anesthesia care in the OR are not universal; audits of infection prevention practices are not routine; and consequently, providers may not have clarity on expected practices and behaviors.Studies have reported problematic practices by anesthesia providers, including use of multiple-dose vials for >1 patient, <100% use of gloves for airway management, failure to perform hand hygiene (HH) after removing gloves, and entry into anesthesia cart drawers without performance of HH.This guidance provides recommendations specific to the anesthesia work area to improve infection prevention through HH, environmental disinfection, and implementation of effective improvement efforts.Furthermore, SHEA acknowledges significant challenges to implementing the array of infection prevention and control recommendations to affect OR culture in general, and the work flow of anesthesia providers in particular.Facility administrators will need to actively collaborate with anesthesia department leaders to build an implementation plan that is timely, comprehensive, and multidisciplinary, and that will allocate hospital resources to educate healthcare personnel and to acquire new infection prevention and control components (eg, single-use laryngoscopes).Facilities should consider this guidance document in revisions of their anesthesia OR policies.This guidance builds on the foundational premise that all facilities where anesthesia services are delivered have formal infection prevention and control programs.Essential elements of these programs include, but are not limited to, policies and procedures for HH, safe preparation and delivery of intravenous medications, and environmental cleaning and disinfection.All individuals involved in these procedures require training appropriate to their tasks, as well as regular skills assessments.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.002

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 head0.030
GPT teacher head0.323
Teacher spread0.293 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations130
Published2018
Admission routes1
Has abstractyes

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