Incidence of nontuberculous mycobacteria in four hot water systems using various types of disinfection
Bibliographic record
Abstract
The objective of this study was to determine the incidence of nontuberculous mycobacteria (NTM) in hot water systems of 4 selected hospital settings. The hospitals provided the following types of disinfection for their hot water systems: hydrogen peroxide and silver, thermal disinfection, chlorine dioxide, and no treatment (control). In each building, 6 samples were collected from 5 sites during a 3 month period. NTM were detected in 56 (46.7%) of 120 samples; the CFU counts ranged from 10 to 1625 CFU/L. The detected NTM species were the pathogens Mycobacterium kansasii, Mycobacterium xenopi, and Mycobacterium fortuitum and the saprophyte Mycobacterium gordonae. The most common to be isolated was M. xenopi, which was present in 51 samples. The hot water systems differed significantly in the incidence of NTM. NTM were not detected in the system treated by thermal disinfection, and a relatively low incidence (20% positive samples) was found in the system disinfected with chlorine dioxide. However, a high incidence was found in the control system with no additional disinfection (70% positives) and in the system using hydrogen peroxide and silver (97% positives). Water temperatures above 50 degrees C significantly limited the occurrence of NTM.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".