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Advances in Vaporized Hydrogen Peroxide Reusable Medical Device Sterilization Cycle Development: Technology Review and Patent Trends

2023· review· en· W4387653876 on OpenAlexaff
M.R. Karimi Estahbanati

Bibliographic record

VenueMicroorganisms · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSterilization (economics)Hydrogen peroxideProcess engineeringMedical deviceTechnology developmentElectricityEnvironmental scienceWaste managementNanotechnologyPulp and paper industryBusinessMaterials scienceChemistryManufacturing engineeringEngineeringBiomedical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Vaporized hydrogen peroxide (VHP) terminal sterilization is one of the most promising techniques for sterilizing temperature-sensitive medical instruments like endoscopes. This technique requires only electricity and sterilant containers to perform the sterilization process in less than 1 h without any substantial safety concerns for patients, personnel, and the environment. This review studies recent advances and future trends in VHP sterilization cycle development using U.S. patent applications and 510(k) premarket notifications. In this regard, the patents focused on increasing VHP concentration or handling residual moisture are discussed in depth. The expired patents are analyzed to introduce existing unencumbered technologies, and active patents are presented to show the most current novelties and technology trends. In addition, 510(k) premarket notifications are explored to evaluate implemented technologies in US market-leading commercial products.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.039
GPT teacher head0.321
Teacher spread0.282 · 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 designNot applicable
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

Citations8
Published2023
Admission routes1
Has abstractyes

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