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Record W2525389202 · doi:10.1111/eos.12295

Is the experience of pain in patients with temporomandibular disorder associated with the presence of comorbidity?

2016· article· en· W2525389202 on OpenAlexaboutno aff
Corine M. Visscher, Erin A. van Wesemael‐Suijkerbuijk, Frank Lobbezoo

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2016
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsTemporomandibular disorderComorbidityMedicineOrofacial painPsychiatryDentistryPhysical therapyTemporomandibular joint

Abstract

fetched live from OpenAlex

The aim of this study was to explore the association between the presence of comorbidities and the pain experience in individual patients with temporomandibular disorder (TMD). This clinical trial comprised 112 patients with TMD pain. For all participants the presence of the following comorbid factors was assessed: pain in the neck; somatization; impaired sleep; and depression. Pain experience was evaluated using the McGill Pain Questionnaire (MPQ). For each subject the TMD-pain experience was assessed for three dimensions - sensory, affective, and evaluative - as specified in the MPQ. The association between comorbid factors and these three dimensions of TMD-pain experience was then evaluated using linear regression models. Univariable regression analyses showed that all comorbid factors, except for one factor, were positively associated with the level of pain, as rated by the sensory description of pain, the affective component of pain, and the evaluative experience of pain. The multivariable regression analyses showed that for all MPQ dimensions, depression showed the strongest associations with pain experience. It was found that in the presence of comorbid disorders, patients with TMD experience elevated levels of TMD pain. This information should be taken into consideration in the diagnostic process, as well as in the choice of treatment.

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)0.007
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 score0.008
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.325
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

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

Citations16
Published2016
Admission routes1
Has abstractyes

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