The Relationship Between Level of Catastrophizing and Mental Health Comorbidity in Individuals With Whiplash Injuries
Bibliographic record
Abstract
OBJECTIVES: Pain catastrophizing has been shown to be correlated with measures of mental health problems such as depression and post-traumatic stress disorder (PTSD). However, the clinical implications of findings reported to date remain unclear. To date, no study has been conducted to determine meaningful cut-scores on measures of catastrophizing indicative of the heightened risk of mental health comorbidity. One objective of the present study was to identify the cut-score on the Pain Catastrophizing Scale (PCS) indicative of the heightened risk of the comorbidity of depression and PTSD. A second objective was to determine whether mental health comorbidity mediated the relationship between catastrophizing and occupational disability. MATERIALS AND METHODS: The sample consisted of 143 individuals with whiplash injuries. Pain severity, pain catastrophizing, depression, and post-traumatic stress symptoms were assessed after admission to a rehabilitation program. Mental health comorbidity was operationally defined as obtaining a score above the clinical threshold on measures of depressive and/or post-traumatic stress symptom severity. RESULTS: A receiver operating characteristic curve analysis revealed that a PCS score of 22 best distinguished between participants with and without mental health comorbidity. Results also revealed that mental health comorbidity mediated the relationship between catastrophizing and occupational disability. DISCUSSION: The findings suggest that a score of ≥22 on the PCS should alert clinicians to the possibility that patients might also be experiencing clinically significant symptoms of depression or PTSD. Greater attention to the detection and treatment of mental health conditions associated with whiplash injury might contribute to more positive recovery outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".