The effect of catastrophizing and depression on chronic pain – a prospective cohort study of temporomandibular muscle and joint pain disorders
Bibliographic record
Abstract
Although most cases of temporomandibular muscle and joint disorders (TMJD) are mild and self-limiting, about 10% of TMJD patients develop severe disorders associated with chronic pain and disability. It has been suggested that depression and catastrophizing contributes to TMJD chronicity. This article assesses the effects of catastrophizing and depression on clinically significant TMJD pain (Graded Chronic Pain Scale [GCPS] II-IV). Four hundred eighty participants, recruited from the Minneapolis/St. Paul area through media advertisements and local dentists, received examinations and completed the GCPS at baseline and at 18-month follow-up. In a multivariable analysis including gender, age, and worst pain intensity, baseline catastrophizing (β 3.79, P<0.0001) and pain intensity at baseline (β 0.39, P<0.0001) were positively associated with characteristic of pain intensity at the 18th month. Disability at the 18-month follow-up was positively related to catastrophizing (β 0.38, P<0.0001) and depression (β 0.17, P=0.02). In addition, in the multivariable analysis adjusted by the same covariates previously described, the onset of clinically significant pain (GCPS II-IV) at the 18-month follow-up was associated with catastrophizing (odds ratio [OR] 1.72, P=0.02). Progression of clinically significant pain was related to catastrophizing (OR 2.16, P<0.0001) and widespread pain at baseline (OR 1.78, P=0.048). Results indicate that catastrophizing and depression contribute to the progression of chronic TMJD pain and disability, and therefore should be considered as important factors when evaluating and developing treatment plans for patients with TMJD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".