MétaCan
Menu
Back to cohort
Record W1995622124 · doi:10.1159/000092224

Effect of Placing a Clear Sealant on the Validity and Reproducibility of Occlusal Caries Detection by a Laser Fluorescence Device: An in vitro Study

2006· article· en· W1995622124 on OpenAlexaff
Chris Deery, J. Iloya, Z.J. Nugent, Visish M. Srinivasan

Bibliographic record

VenueCaries Research · 2006
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsReproducibilitySealantDentistryMolarMedicineEnamel paintOrthodonticsMaterials scienceChemistry

Abstract

fetched live from OpenAlex

The principal aim of this in vitro study was to assess the validity and reproducibility of the detection of occlusal caries using laser fluorescence (LF), prior to and following the placement of a clear fissure sealant. It also aimed to compare the manufacturer's standard cut-off recommendations with those published for in vitro studies and to compare the validity and reproducibility of LF with clinical visual examination (CVE) for the detection of occlusal caries under sealants. Three clinicians independently examined visually and with LF 37 extracted teeth (25 molars, 12 premolars), with a range of clinical caries from apparently sound to cavitated dentinal caries. Examinations were conducted under dental surgery conditions. Subsequently, the teeth were serially sectioned to provide the validating criterion. Following placement of the sealant, the specificity generally increased but there was an associated loss of sensitivity, at both the D1 (enamel and dentine) and D3 (dentine) diagnostic thresholds. The LF readings were significantly lower after placement of the sealant (p<0.05). The manufacturer's recommended cut-offs appear to be the most appropriate to use. The CVE had superior validity and reproducibility when compared to LF. Overall, the placement of a clear sealant does influence the detection of caries by LF but does not prevent the detection of caries by this method.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.583
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
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 head0.050
GPT teacher head0.393
Teacher spread0.342 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCaries ResearchSame topicDental Health and Care UtilizationFrench-language works237,207