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Record W160725490

Survey of nasopharyngoscope decontamination methods in Canada.

2010· article· en· W160725490 on OpenAlexaffabout
Maria K. Brake, Boyd S Lee, Loren Savoury, Jonathan Cavanagh, Kenneth J. Burrage, Thomas J. Smith, Timothy Brown

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHumanitiesPolitical scienceMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: nasopharyngoscopes are essential tools in modern otolaryngology practice. Owing to their frequent and diverse use, it is important to ensure that they can be efficiently and thoroughly cleaned. To date, there are no official national guidelines provided by the Canadian Society of Otolaryngology-Head and Neck Surgery (CSOHNS) for decontamination of nasopharyngoscopes. OBJECTIVE: to compare flexible nasopharyngoscope decontamination practices across Canada. METHODS: a questionnaire regarding nasopharyngoscope cleaning procedures was distributed online to all otolaryngologists registered with the CSOHNS. The survey was anonymous. Topics addressed province, practice type, maintenance, operations, ventilation, and process development. RESULTS: thirty-five percent of the 505 Canadian otolaryngologists contacted participated in the survey. Automated sterilization of nasopharyngoscopes is employed by 16% of participants, of which the majority of this use is in hospital settings. Over 61.3% of participants use a multistep decontaminating soak for cleaning. Decontamination procedures were created within the department in 59% of cases, and over 28.3% of participants are unsure as to whether their procedures adhere to infectious disease and industry standards. CONCLUSION: various procedures are employed throughout Canada owing to a lack of standardization. Survey responses indicate that Canadian otolaryngologists would appreciate a national standard for the cleaning of flexible nasopharyngoscopes, particularly for nonhospital practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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