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Record W3016999060 · doi:10.1111/eje.12533

Dental sleep medicine: Time to incorporate sleep apnoea education in the dental curriculum

2020· article· en· W3016999060 on OpenAlex
Alberto Herrero Babiloni, Gabrielle Beetz, Cibele Dal Fabbro, Marc O. Martel, Nelly Huynh, Jean-François Massé, Barry J. Sessle, Gilles Lavigne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal Of Dental Education · 2020
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of TorontoUniversité LavalMcGill UniversityUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersCanadian Institutes of Health Research
KeywordsSleep medicineCurriculumSleep (system call)MedicineContext (archaeology)Dental educationMedical educationSleep disorderPsychiatryDentistryPhysical therapyPsychologyInsomniaPedagogy

Abstract

fetched live from OpenAlex

Dental sleep medicine is a discipline that includes conditions such as sleep breathing disorders (eg snoring and sleep apnoea), sleep bruxism, orofacial pain and sleep-related complaints, and to some extent gastro-oesophageal reflux disorder and/or insomnia. Obstructive sleep apnoea (OSA) is a life-threatening condition that dentists need to identify and manage when indicated in order to increase patient well-being and to be taken in consideration in the dental curriculum. The main objective of this paper is to highlight the relevance of dental sleep medicine in the context of dental education, and to discuss potential educational content for integration in the dental curriculum with a focus on OSA, a condition that is not yet integrated in many dental training curricula around the world.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.014
GPT teacher head0.302
Teacher spread0.287 · 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