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Record W4200498450 · doi:10.1080/20476965.2021.2004933

A simulation model to analyse automation scenarios in decontamination centers

2021· article· en· W4200498450 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsUniversité LavalPolytechnique Montréal
Fundersnot available
KeywordsHuman decontaminationAutomationProcess (computing)Work (physics)Computer sciencePatient safetyProductivitysortRisk analysis (engineering)Operations managementEngineeringMedicineWaste managementHealth careDatabase

Abstract

fetched live from OpenAlex

Decontamination centres provide sterilisation services (sort, disinfect, package, and sterilise) for reusable surgical instruments that have a vital impact on patient safety. The market trend is to increase the level of automation in the decontamination process, to increase productivity, and reduce the risk of human error and musculoskeletal injuries. The goal of this research is to study the use of automated guided vehicles (AGVs) in sterilisation departments, to improve safety and efficiency. A generic simulation model is created based on data gathering of various decontamination centres and is validated for a specific centre to analyse various aspects of applying AGVs to automate the internal transfer. Centre's potential to increase capacity through AGV application is analysed and a Design of Experiments is conducted to identify the most promising implementation scenarios. Results show reductions in treatment time and work in process, while ,maintaining the accessibility of medical instruments, and ensuring worker safety.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.305
Teacher spread0.277 · 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