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Record W4392633007 · doi:10.1080/08869634.2024.2323424

Depression and the risk of developing temporomandibular disorders in different diagnostic groups: A systematic review with meta-analysis

2024· review· en· W4392633007 on OpenAlexaff
Elisa de Almeida Hoff, Rafaela Krieger Grossi, Lucas Bozzetti Pigozzi, Caroline Hoffmann Bueno, Marcos Pascoal Pattussi, Tainá Rossi, Tatiana Quarti Irigaray, João Batista Blessmann Weber, Márcio Lima Grossi

Bibliographic record

VenueCRANIO® · 2024
Typereview
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDepression (economics)Meta-analysisResearch Diagnostic CriteriaMedicineOsteoarthritisSystematic reviewRisk factorPhysical therapyClinical psychologyInternal medicineMEDLINEChronic painAlternative medicinePathologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the role of depression in the development of TMD groups. METHODS: This systematic review with meta-analysis compared the prevalence and scores of depression between TMD groups and controls. RESULTS: The results showed that depression was a significant risk factor in the development of RDC/TMD axis I muscle disorders (group I) and arthralgia/osteoarthritis/osteoarthrosis (group III), and non-significant for disc displacements (group II). Severe depression had almost four times the risk of developing TMD as compared to moderate depression. CONCLUSION: These findings suggest that addressing psychological factors in general, and depression in particular, in the managemenof TMD is crucial, especially in those TMD groups with higher pain levels (I and III), and the TMD pain reduction is crucial in reducing depression levels.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.023
Bibliometrics0.0050.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.392
Teacher spread0.332 · 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 designSystematic review
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

Citations17
Published2024
Admission routes1
Has abstractyes

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