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Record W2343711207 · doi:10.15446/rsap.v18n1.47613

Prevalencia de la disfunción temporomandibular en trabajadores de la industria. Asociación con el estrés y el trastorno del sueño

2016· article· es· W2343711207 on OpenAlexaboutno aff
Ronald Jefferson Martins, Cléa Adas Salíba Garbin, Nádia Biage Cândido, Artênio José Ísper Garbín, Tânia Adas Saliba

Bibliographic record

VenueRevista de Salud Pública · 2016
Typearticle
Languagees
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHumanitiesGynecologyPhilosophy

Abstract

fetched live from OpenAlex

Objective To determine the presence of temporomandibular disorders (TMD), sleep disorders and stress, as well as the association between these factors, among industrial workers in São Paulo State, Brazil. Material and Methods Fonseca's questionnaire was used to verify the level of TMD, the Toronto Sleep Assessment Questionnaire (SAQ) was applied to check the quality and occurrence of sleep disorders, and the Social Readjustment Rating Scale (SRRS) was used to check the degree of stress. The data collected were tabulated with Epi InfoTM 7 and statistically analyzed using the chi-square test, with a 5 % significance level. Results 104 workers participated in the survey. Most were male (74 %) between 35 and 44 years of age (26 %). Thirty-seven (35.6 %) had some degree of disorder, 65 (62.5 %) presented with sleep disorders, and 6 (5.8 %) presented with higher degrees of stress. After statistical analysis, there was no significant association between stress and temporomandibular disorders (TMD). However, there was an association between quality of sleep and sex of the individual with TMD (p<0.01). Conclusion We conclude that a high percentage of the analyzed population has sleep disorders and TMD. Sex and the quality of sleep influence the occurrence of TMD.

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)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.374
Teacher spread0.357 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRevista de Salud PúblicaSame topicTemporomandibular Joint DisordersFrench-language works237,207