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Water treatment and monitor disinfection

2006· review· en· W2096548180 on OpenAlexvenueno aff
Gianni Cappelli, Marco Riccardi, Salvatore Perrone, Moreno Bondi, Giulia Ligabue, A Albertazzi

Bibliographic record

VenueHemodialysis International · 2006
Typereview
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsDisinfectantMedicineQuality assuranceWaste management

Abstract

fetched live from OpenAlex

Water treatment system and dialysis monitors are susceptible to microbial contaminations and periodical disinfection procedures are mandatory to obtain results requested from international standards and guidelines. Several chemical germicides or some physical treatments are on the market validated by device manufacturer according to medical device directives. With time, interfering substances from dialysis device or water are able to modify disinfection efficiency. Simulating-use testing is not a common procedure to validate disinfectants and recent data document as biofilm represents the most important cause of disinfection inefficacy. Some international standards include tests in the presence of various interfering substances but their use is not widespread. When using a disinfectant, residue toxicity, material compatibility and potential risks for the staff also have to be considered. A quality assurance program has to be implemented to obtain adequate performances and to improve results on patients.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.001

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 head0.025
GPT teacher head0.312
Teacher spread0.287 · 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 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

Citations25
Published2006
Admission routes1
Has abstractyes

Explore more

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