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Record W3094048080 · doi:10.1002/jdd.12461

Orofacial pain education in dentistry: A path to improving patient care and reducing the population burden of chronic pain

2020· review· en· W3094048080 on OpenAlexaff
Béatrice P. De Koninck, Sherif M. Elsaraj, Fernando G. Exposto, Alberto Herrero Babiloni, Flavia P. Kapos, Sonia Sharma, Akiko Shimada

Bibliographic record

VenueJournal of Dental Education · 2020
Typereview
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsMcGill UniversityJewish General HospitalUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineOrofacial painChronic painDental carePopulationPhysical therapyDentistry

Abstract

fetched live from OpenAlex

Dentists stand in an optimal position to prevent and manage patients suffering from chronic orofacial pain (OFP) disorders, such as temporomandibular disorders, burning mouth syndrome, trigeminal neuralgia, persistent idiopathic dentoalveolar pain, among others. However, there are consistent reports highlighting a lack of knowledge and confidence in diagnosing and treating OFP among dental students, recent graduates, and trained dentists, which leads to misdiagnosis, unnecessary costs, delay in appropriate care and possible harm to patients. Education in OFP is necessary to improve the quality of general dental care and reduce individual and societal burden of chronic pain through prevention and improved quality of life for OFP patients. Our aims are to emphasize the goals of OFP education, to identify barriers for its implementation, and to suggest possible avenues to improve OFP education in general, postgraduate, and continuing dental education levels, including proposed minimum OFP competencies for all dentists. Moreover, patient perspectives are also incorporated, including a testimony from a person with OFP. General dentists, OFP experts, educators, researchers, patients, and policy makers need to combine efforts in order to successfully address the urgent need for quality OFP education.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.403
Teacher spread0.381 · 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

Citations46
Published2020
Admission routes1
Has abstractyes

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