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Record W7162029479 · doi:10.82308/6139

Contribution of sleep apnea, fatigue, and insomnia on the transition from acute to chronic painful temporomandibular disorders (TMDs) and its persistence: A prospective cohort study

2022· dissertation· en· W7162029479 on OpenAlexaboutno aff
Sherif Elsaraj

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsProspective cohort studyEpworth Sleepiness ScaleInsomniaChronic painObstructive sleep apneaCohort studyCohortFibromyalgia

Abstract

fetched live from OpenAlex

ABSTRACT Background: Previous studies have demonstrated that insomnia, obstructive sleep apnea (OSA), and fatigue are associated with chronic temporomandibular disorders (TMD)-related pain. TMD pain is a group of a musculoskeletal condition affecting the muscles of mastication, the temporomandibular joints, or both. The International Association for the Study of Pain (IASP) defines chronic pain as “persistent or recurrent pain lasting longer than 3-months” and is associated with significant dysfunction.Objective: This prospective 3-month cohort study aimed to determine whether insomnia, OSA, and fatigue are associated with the transition from acute to chronic TMD-related pain as well as its’ persistence, when chronic pain is defined by: (i) pain duration (> 3 months), and (ii) dysfunction (Graded Chronic Pain Scale (GCPS II-IV). Methods: Acute (≤ 3 months) and Chronic (> 3 months) TMD-related pain subjects were recruited between 2015 to 2021, through four different sites in Montreal and Ottawa. Subjects received a clinical examination and completed questionnaires at baseline and 3-month follow-up. A diagnosis was obtained using the Research Diagnostic Criteria or the Diagnostic Criteria for Temporomandibular Disorders. At baseline, insomnia, OSA, and fatigue were assessed using the Insomnia Severity Scale (ISI), the Epworth Sleepiness Scale (ESS), and the Fatigue Severity Scale (FSS), respectively. Subjects completed GCPS form at baseline and 3-month follow-up.Results: From 454 subjects recruited 376 completed the follow-up. Borderline associations were found between OSA and the transition or persistent risk when chronic pain was defined by pain duration (RRadjusted_duration = 1.11, P = 0.07) and dysfunction (RRadjusted_dysfunction =1.40, P = 0.052), contrary to insomnia (RRadjusted_duration = 0.94, P = 0.27, RRadjusted_dysfunction =1.00, P = 0.99). The secondary analyses found that OSA was specifically associated with the persistence of TMD-related pain (RR = 1.13, P = 0.04). Fatigue was not associated with increased risk of transition or persistence risk at 3-month follow-up when chronic pain was defined by duration (RRadjusted =1.01, P = 0.99). However, when chronic pain was defined as dysfunction, fatigue was associated with an increased transition or persistence risk (RRadjusted = 1.72, P = 0.002). Results indicate that fatigue and OSA are contributing factors to the transition or persistence of TMD-related pain. These results suggest that OSA and fatigue assessment may be considered as part of the comprehensive clinical exam for TMD patients, and that their management should be tested to prevent the transition and persistence of TMD-related pain

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.332
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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