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Day surgery visits for dental problems

2009· article· en· W1997336923 on OpenAlexaffabout
Carlos Quiñonez, Debbie Gibson, Aleksandra Jokovic, David Locker

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsGovernment of OntarioMinistry of Health and Long Term CareUniversity of Toronto
Fundersnot available
KeywordsMedicineDental careAmbulatoryAmbulatory careOral healthHealth careFamily medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To fill an information gap for dental care policy stakeholders in Canada, this pilot study explored the nature of day surgery (DS) visits for dental problems in Ontario, the country's largest province. METHODS: The Canadian Institute for Health Information's National Ambulatory Care Reporting System was used, which contains demographic, diagnostic, procedural and administrative information for ambulatory care settings across Ontario. Fiscal years 2003/2004 to 2005/2006 data were included for DS visits that had a main problem coded with an International Classification of Diseases code in the range K00-K14, representing diseases of the oral cavity, salivary glands and jaws. RESULTS: During this period, approximately 75 791 persons made 79 133 DS visits for dental problems in Ontario. Proportionally, children under 5 years of age with dental caries represent the majority of DS visits. Restorations and extractions were the most frequently performed DS care procedure. CONCLUSIONS: This is the first study of its kind in Canada, and confirms many of the assumptions held about DS care for dental problems. The study also acts as a baseline for ongoing quality improvement and planning within the province of Ontario.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.210
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.105
GPT teacher head0.393
Teacher spread0.289 · 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

Citations6
Published2009
Admission routes2
Has abstractyes

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