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Record W2313694280 · doi:10.2345/0899-8205-50.s2.18

Safe to Handle? Comparing Manually and Machine-Washed Medical Devices

2016· article· en· W2313694280 on OpenAlexaff
Kaumudi Kulkarni, Dzelma Kaczorowski, Alex Bonkowski, Stephen J. Kovach, Ralph Basile

Bibliographic record

VenueBiomedical Instrumentation & Technology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsFraser Health
Fundersnot available
KeywordsMedical deviceSAFERSingle useComputer scienceRendering (computer graphics)Sterilization (economics)Medical equipmentProcess engineeringReliability engineeringBiomedical engineeringEngineeringMedicineComputer securityArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

The goal of medical device reprocessing is to ensure that a given device is ready for safe use on the next patient. Effective cleaning of devices is critical to achieving this goal. Device cleaning also has had an ancillary goal: rendering the device safe for handling with ungloved hands by sterile processing personnel. Such personnel are tasked with preparing the device for further reprocessing steps, such as packaging for sterilization. This raises the question: Are manually cleaned devices as safe to handle as machinewashed medical devices? The current study sought to gather data to help answer this question. Further, this study sought to evaluate the effectiveness of a relatively new tool in the area of decontamination in healthcare settings—ultraviolet (UV) disinfection—and whether UV disinfection could effectively and efficiently be used to render manually cleaned devices safer to handle. Mechanically washed reusable medical devices generally are considered to have a more reliable and effective level of cleanliness, as supported by the limited research available in this area.1 The underlying logic for this assertion is that mechanical methods are more robust and reliably repeatable compared with manual methods.2 Machine washing allows for the use of cleaning solutions at much higher temperatures. These higher temperatures typically include a detergent wash at 60°C (150°F), followed by thermal disinfection at 82°C to 93°C (180°F to 195°F) for one minute or longer.3 Further, the cycle settings are programmed into a machine and are repeated cycle after cycle. Conversely, manual cleaning depends on the individual performance of the person conducting the cleaning. These assertions do not devalue the importance of reprocessing staff. To the contrary, even the most simply designed medical device requires proper and effective precleaning by manual means. Typically speaking, the more complex the device, the more important the manual steps to prepare the device for further cleaning by mechanical means. Further, a substantial number of medical devices cannot undergo mechanical cleaning. This is due to the material used in construction (i.e., thermolabile, nonsubmersible) and/or the complexity of the design.4 Effective cleaning of these devices is wholly reliant on manual processes. By design, sterile processing departments typically have a physical separation between the area where items are cleaned (the “dirty side”) and the area to which they are transported for further processing, such as sterilization (the “clean side”). Mechanical cleaning equipment typically is designed with a dual door design, where the loading door is located on the dirty side and the unloading door on the clean side. For manually cleaned devices, and for passing items back from the clean to the dirty side for recleaning, sterile processing departments typically have a “pass-through” window. Safe to Handle? Comparing Manually and Machine-Washed 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 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)0.013
metaresearch head score (Gemma)0.073
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.001
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 head0.017
GPT teacher head0.274
Teacher spread0.258 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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