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Record W4388693679 · doi:10.1186/s12903-023-03590-y

Dental unit waterline testing practices: an 11-Year retrospective study

2023· article· en· W4388693679 on OpenAlexafffundabout
Juan M. Buitrago, Rob J. Kolbe, Michelle F. Siqueira

Bibliographic record

VenueBMC Oral Health · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research FoundationUniversity of Saskatchewan
KeywordsMedicineWaterlineDentistryTest (biology)Engineering

Abstract

fetched live from OpenAlex

OBJECTIVES: This retrospective study examined the dental unit waterline (DUWL) testing practices of Saskatchewan dental clinics over a period of 11 years, with an emphasis on their responses after identification of high microbial levels. MATERIALS AND METHODS: Dental clinics (n = 137) aseptically collected samples of output water from their air/water syringes, handpieces, and ultrasonic scaler lines using Sigma-Aldrich® waterline test kits and delivered them to a quality assurance laboratory. Tests were incubated for seven days at room temperature, and those with heterotrophic plate counts > 500 CFU/mL were reported as failures. Statistical analyses were performed on a database containing 4,093 test results. RESULTS: =.52, p < 0.001) was found between clinics' DUWL testing frequency and failure rate. Only 7% of failed DUWL tests were followed up by collection of a subsequent test within two weeks, of which 47% still exceeded the 500 CFU/mL threshold. CONCLUSIONS: Our findings demonstrate an association between DUWL testing frequency and detection of unacceptable microbial levels, along with infrequent retesting and often-inadequate intervention after a failed test. This suggests the need for further efforts at the regulatory and educational levels to maintain adequate water quality during dental treatment. CLINICAL RELEVANCE: Procedural water can become contaminated in DUWLs and endanger patients. Regular DUWL monitoring and evidence-based interventions to treat contaminated systems are necessary to safeguard patient health.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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.

Opus teacher head0.165
GPT teacher head0.435
Teacher spread0.270 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2023
Admission routes3
Has abstractyes

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