Impact of Catastrophizing in Patients with Temporomandibular Disorders—A Systematic Review
Bibliographic record
Abstract
AIMS: To assess the prevalence of catastrophizing in patients with temporomandibular disorders (TMD) and the possible associations between catastrophizing and treatment outcome. METHODS: This review was registered in the Prospero database (CRD42018114233). Electronic searches were performed in PubMed, Scopus, and PsycINFO from the inception of each database up to October 26, 2018, and were combined with a hand search. Articles focusing on levels of catastrophizing and how catastrophizing affects pain levels and treatment outcomes for patients diagnosed with TMD were included, as well as studies reporting how treatment outcomes were affected by cognitive behavioral treatment as an addition to standard treatment for TMD. Reviews and case reports were excluded. Risk of bias was assessed with the Newcastle-Ottawa scale. RESULTS: The literature search identified 266 articles. After screening of abstracts, the full texts of 59 articles were assessed. Of these, 37 articles, including 4,789 patients with TMD and 6,617 controls, met the inclusion criteria. Higher levels of pain catastrophizing were reported in patients with TMD, with a large effect size (Hedges' g = 0.86) compared to pain-free controls. Furthermore, associations of higher levels of catastrophizing with higher symptom severity and with poorer treatment outcome were reported together with indications of positive effects from cognitive behavioral therapy. CONCLUSION: The results suggest an association between catastrophizing and TMD that may affect not only symptom severity but also treatment outcome. Assessing levels of pain catastrophizing might therefore be valuable in the assessment and management of patients with TMD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".