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Record W1987661385 · doi:10.1086/529587

Economic Analysis of Reprocessing Single-Use Medical Devices: A Systematic Literature Review

2008· review· en· W1987661385 on OpenAlexaff
Philip Jacobs, Julie Polisena, David Hailey, Susan Lafferty

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2008
Typereview
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsCapital District Health AuthorityCanadian Agency for Drugs and Technologies in HealthInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsReuseSingle useIncentiveMedicineMedical literatureCost–benefit analysisQuality (philosophy)Risk analysis (engineering)Systematic reviewMEDLINEComputer scienceEngineeringProcess engineeringWaste managementPathologyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Although an increasing number of medical devices are labeled "for single use only," cleaning and reuse of single-use medical devices continues, because of the economic incentive. We conducted a survey of the economic literature to obtain the current evidence available and to assess the costs and benefits of reusing single-use medical devices. METHODS: A comprehensive literature search was carried out to identify articles that compared single use and reuse of single-use medical devices and that met specific scientific criteria, including evaluation of economic outcomes. Each selected article was independently reviewed by 2 reviewers to extract cost and clinical outcome data and to assess the quality of the study. RESULTS: Nine published articles met the selection criteria. The savings were about 49% of the direct cost. These savings would be offset by adverse-event costs, but none were detected. However, quality of the studies was generally poor. CONCLUSIONS: There is little available evidence of quality in the published literature to assess the practice of reuse of single-use medical devices. Moreover, data on clinical outcomes are missing and, where available, cannot be attributed specifically to the reuse of single-use medical devices.

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)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.034
GPT teacher head0.335
Teacher spread0.301 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations39
Published2008
Admission routes1
Has abstractyes

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