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Clinical Decision‐Making for Caries Management in Root Surfaces

2001· article· en· W2123585503 on OpenAlexaff
James L. Leake

Bibliographic record

VenueJournal of Dental Education · 2001
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRoot cariesDentistryMedicineCarious lesionNatural historyIncidence (geometry)PopulationFluoride varnishChlorhexidineLesionSurgeryInternal medicineEnvironmental healthMathematics

Abstract

fetched live from OpenAlex

This report presents the results of an evidence-based approach to obtaining the best available information on the natural history, prevalence, incidence, diagnosis, and treatment of root caries. Searches of electronic databases produced 807 references; from these and from citations in the selected articles, a final 161 references were used. We found that the information on the natural history of the disease does not provide practitioners with probabilities of, or time estimates for, progression of the disease through stages. For patients aged thirty and older, the prevalence of root caries is roughly 20 to 22 percent less than a person's age. Severity reaches over one lesion by age fifty, two lesions by age seventy, and just over three lesions for those seventy-five and older. About 8 percent (odds of 1:11) of the population would be expected to acquire one or more new root caries lesions in one year. The accuracy of current systems of diagnosis is unknown, although color has been shown to have little validity. Using the criteria of "softness" to define active lesions has been validated by the presence of microbes in the lesion. One strong study and other studies with weaker design or shorter duration add consistent support for the use of fluorides in the remineralization of root caries. Every three-month application of chlorhexidine varnish was shown to be efficacious in one arm of one study. Evidence for restoration of root caries is tentative since the studies were of limited design and duration.

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 score0.351
Threshold uncertainty score0.298

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 head0.034
GPT teacher head0.444
Teacher spread0.410 · 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

Citations46
Published2001
Admission routes1
Has abstractyes

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