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Record W3081377198 · doi:10.1111/joor.13075

The face of Dental Sleep Medicine in the 21st century

2020· review· en· W3081377198 on OpenAlexaff
Frank Lobbezoo, Gilles Lavigne, Takafumi Kato, Fernanda R. Almeida, Ghizlane Aarab

Bibliographic record

VenueJournal of Oral Rehabilitation · 2020
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsSleep medicineSleep (system call)MedicineSleep BruxismCurriculumPsychiatrySleep disorderPsychologyDentistryInsomnia

Abstract

fetched live from OpenAlex

It becomes increasingly clear that some sleep disorders have important diagnostic and/or management links to the dental domain, hence the emergence of the discipline 'Dental Sleep Medicine'. In this review, the following topics are discussed: 1. the reciprocal associations between oro-facial pain and sleep; 2. the associations between sleep bruxism and other sleep-related disorders; 3. the role of the dentist in the assessment and management of sleep bruxism; and 4. the dental management of obstructive sleep apnoea. From these topics' descriptions, it becomes clear that the role of the dentist in the recognition and management of sleep-related oro-facial pain, sleep bruxism and obstructive sleep apnoea is large and important. Since many dental sleep disorders can have severe consequences for the individual's general health and well-being, it is imperative that dentists are not only willing to take on that role, but are also able to do so. This requires more attention for Dental Sleep Medicine in the dental curricula worldwide, as well as better postgraduate training of dentists who are interested in specialising in this intriguing domain. This review contributes to increasing the dental researcher's, teacher's and care professional's insight into the discipline 'Dental Sleep Medicine' as it has taken shape in the 21st century, to the benefit of all patients suffering from dental sleep disorders.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.392
Teacher spread0.352 · 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 designOther design
Domainnot available
GenreReview

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

Citations48
Published2020
Admission routes1
Has abstractyes

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