Mycobacterium hassiacum: a thermophilic Mycobacterium species to demonstrate thermal disinfection of medical devices
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".