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Record W3184730964 · doi:10.12968/denu.2021.48.7.547

Cleft lip and palate in general dental practice: filling in the gaps

2021· article· en· W3184730964 on OpenAlexaff
Sharan Reddy, Catherine Liu, Mina Vaidyanathan, Nabina Bhujel

Bibliographic record

VenueDental Update · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineEtiologyCraniofacialDentistryPsychosocialClinical significanceClinical PracticeDental practiceOrthodonticsFamily medicinePathology

Abstract

fetched live from OpenAlex

Cleft lip and palate (CLP) is the most common of craniofacial anomalies in humans. CLP results from disruption of embryonic processes during orofacial development; while syndromic clefts may have clearer aetiology, non-syndromic clefts are heterogeneous in aetiology. It is important for GDPs to understand the classification of CLP and the structure of centralized cleft centres in order to communicate with cleft teams. This article aims to clarify the role of GDPs within cleft management and discusses challenges in maintaining oral health in this group of patients, including increased risk of dental caries and periodontal disease, dental anomalies and psychosocial considerations. CPD/Clinical Relevance: The GDP should be responsible for maintaining good oral health for the patient with cleft lip and palate, focusing on prevention from a young age and restorative work if needed.

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 score0.166
Threshold uncertainty score0.324

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 head0.012
GPT teacher head0.305
Teacher spread0.293 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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