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Record W4392706467 · doi:10.51731/cjht.2024.854

Reprocessed Single-Use Semicritical and Critical Medical Devices

2024· article· en· W4392706467 on OpenAlexaboutno aff
Kellee Kaulback, Jennifer Horton

Bibliographic record

VenueCanadian Journal of Health Technologies · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsBioburdenMedical deviceSingle useMedicineMedical emergencyPatient safetyRisk analysis (engineering)BusinessOperations managementHealth careSurgeryEngineering

Abstract

fetched live from OpenAlex

What Is the Issue? Reprocessing a medical device includes cleaning, reconditioning, testing, and disinfection to ensure the device can safely be reused. In contrast to reusable medical devices, manufacturers are not required to provide instructions for properly cleaning and sterilizing single-use medical devices (SUMDs). Health Canada regulates third-party device reprocessors and requires they meet the same requirements as new device manufacturers. Health Canada does not provide oversight for hospital onsite reprocessing, deferring to the oversight provided at the provincial and territorial levels. Given the potential economic and environmental benefits of using reprocessed SUMDs, there is a growing interest in determining the clinical safety of reprocessed SUMDs. Current standards for reprocessing medical devices use definitions for sterilization and disinfection based on measurement of bioburden, but not necessarily clinical outcomes such as infection. What Did We Do? To inform decisions about the appropriate use of reprocessed critical and semicritical SUMDs, CADTH sought to identify and summarize literature evaluating the clinical safety of reprocessed SUMDs, defined as infections, mortality, or other adverse events, compared with nonreprocessed (new) SUMDs. Microbiological outcomes, such as bacterial colony counts, were not included. An information specialist searched for peer-reviewed and grey literature sources. This report does not provide a comprehensive list of device reprocessors in Canada or recommend any specific methods of reprocessing medical devices. What Did We Find? We identified 8 studies, including one study based in Canada, that evaluated the use of reprocessed SUMDs compared with new SUMDs; most did not report statistically significant differences in patient outcomes between groups. Most of the included studies were of very low to moderate quality, which limits confidence in the observed outcomes resulting from the reuse of these devices. Half of the included studies were published before the year 2005, which may limit applicability given potential improvements and changes over time in reprocessing standards, surgical approaches, device specifications, and patient care protocols. Most of the studies evaluated a different type of reprocessed single-use medical device for different surgical populations, so there is very limited evidence for the use of a specific device in a specific population or intervention of interest. All included studies evaluated SUMDs classified as critical, and all were conducted in surgical settings; however, it is unclear whether patient risk levels would be different for semicritical devices or in nonsurgical settings. What Does it Mean? Given various devices, clinical applications, and reprocessing methods, it is difficult to draw broad conclusions about the appropriateness of reprocessing SUMDs. While the evidence base in this review was insufficient to conclude whether reprocessed critical SUMDs in surgical settings affect patient outcomes, Canadian standards and other resources exist to help inform decisions around medical device reprocessing based on infection risk. To ensure patient safety, any reprocessing of SUMDs should meet standards for safety, effectiveness, and labelling that follow Health Canada regulations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.313
Teacher spread0.281 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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