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Record W2995732406 · doi:10.1111/joor.12923

Comorbid conditions associated with painful temporomandibular disorders in adolescents from Brazil, Canada and France: A cross‐sectional study

2019· article· en· W2995732406 on OpenAlexafffundabout
Khurram J. Khan, Michèle Muller‐Bolla, Oscar Anacleto Teixeira, Mervyn Gornitsky, Antônio Sérgio Guimarães, Ana Míriam Velly

Bibliographic record

VenueJournal of Oral Rehabilitation · 2019
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
FundersJewish General HospitalMcGill University
KeywordsMedicineCross-sectional studyNiceHeadachesComorbidityLogistic regressionPhysical therapyMigraineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Painful temporomandibular disorder (TMD) is common among adolescents. Presence of painful comorbidities may worsen painful TMD and impact treatment effectiveness. OBJECTIVE: The aim of this study was to assess the association between painful TMD and comorbidities. METHODOLOGY: In this cross-sectional study, adolescents were recruited in Montreal (Canada), Nice (France) and Arceburgo (Brazil). Reliable instruments were used to assess painful TMD and comorbidities. Multivariable logistic and linear regression analyses were conducted to assess the study aims. RESULTS: The prevalence of self-reported painful TMD was estimated at 31.6%; Arceburgo (31.6%), Montreal (23.4%) and Nice (31.8%). Painful TMD was more common among girls than boys (OR = 1.96). Painful TMD was associated with a higher number of comorbidities (OR = 1.77); Arceburgo (OR = 1.81), Montreal (OR = 1.80) and Nice (OR = 1.72). A stronger association was found between painful TMD and headaches (OR = 4.09) and a weaker one with stomach pain (OR = 1.40). Allergies were also related to painful TMD (OR = 1.43). CONCLUSION: Painful TMD was associated with comorbidities. Headaches were consistently associated with painful TMD. Other associations were modified by sex and/or covariates related to the cities where participants were recruited.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.561
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.340
Teacher spread0.331 · 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

Citations18
Published2019
Admission routes3
Has abstractyes

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