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Record W3108378831 · doi:10.1111/ipd.12752

Restorative thresholds for primary and permanent molars in children: French dentist decisions

2020· article· en· W3108378831 on OpenAlexaff
Michèle Muller‐Bolla, Elody Aïem, Cyril Coulot, Ana Míriam Velly, Sophie Doméjean

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineMolarDentistryPaediatric dentistryPermanent teethCarious lesionPopulationOrthodonticsEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Worldwide, numerous surveys have investigated practices and knowledge about caries management in adults, but few are available for children. AIM: The present cross-sectional survey aimed to assess the restorative thresholds (RTs) in primary and permanent molars in children used by a population of dentists treating children and practicing in France. DESIGN: The study population consisted of French dentists treating children (Fr-DTCs) who were registered in the French Society of Pediatric Dentistry (n = 250). A specific questionnaire was developed. Descriptive and statistical analyses were performed. RESULTS: Response rate was 80.4% (n = 201). Considering that an appropriate RT is at the stage of a moderate lesion (occlusal: International Caries Detection and Assessment System 4; approximal: lesion involving the external third of dentine), more than 50% of respondents showed a tendency for iatrogenic treatment, except for occlusal carious lesions in primary molars. Inappropriate invasive strategies were more often reported for occlusal lesions in permanent than primary molars. Moreover, for both molar types, these inappropriate RTs were more often chosen for approximal than occlusal lesions. CONCLUSIONS: The present survey suggested that Fr-DTCs tend to overtreat in terms of caries management in both primary and permanent molars.

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)0.000
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 score0.025
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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