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Record W3012158333 · doi:10.1186/s13104-020-04978-7

Mycobacterium hassiacum: a thermophilic Mycobacterium species to demonstrate thermal disinfection of medical devices

2020· article· en· W3012158333 on OpenAlexaff
B. Haas, Kelly J. Soto, Dana S. Day, Alexander C. Roy, Marie‐Claude Gagnon, Jodi R. Alt, Philippe Labrie

Bibliographic record

VenueBMC Research Notes · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsLog reductionThermophileMycobacteriumMicrobiologyMedical deviceStrain (injury)Thermal resistanceFood and drug administrationMedicineVeterinary medicineBiologyBacteriaThermalPathologyBiomedical engineeringPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: Reprocessing reusable medical devices is crucial in the healthcare industry. To ensure patient safety, strict standards are dictated to validate thermal disinfection in automated washer-disinfectors. The United States Food and Drug Administration (FDA) has specific recommendations on the vegetative bacterial challenge but comparatively vague guidance on the use of a thermophilic Mycobacterium strain for thermal disinfection studies. This study aims to compare thermal resistance of Mycobacterium hassiacum and Mycobacterium terrae and determine which strain is suitable for medical device thermal disinfection validation testing in automated washer-disinfectors. RESULTS: Thermal resistance was demonstrated in vitro by calculating D-values for each strain at different exposure temperatures, and correlated with actual in situ processing conditions. M. terrae was completely killed (> 7 log reduction) at temperatures above 68 °C, with D-values between 46.6 and 27.8 s at temperatures between 59.5 and 67.2 °C. M. hassiacum was completely killed (> 8 log reduction) at temperatures above 75 °C, with D-values between 82.1 and 21.7 s at temperatures ranging between 69.2 and 73.6 °C. In vitro results were correlated in a washer-disinfector performance validation setup.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.144
GPT teacher head0.375
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations5
Published2020
Admission routes1
Has abstractyes

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