Catastrophizing, pain and traumatic stress symptoms following burns: A prospective study
Bibliographic record
Abstract
BACKGROUND: Pain and posttraumatic stress disorder (PTSD) symptoms are significant problems in the aftermath of a burn injury and they often co-occur. Catastrophizing has been linked to both phenomena. The aim of this study was to investigate the underlying role of catastrophizing in PTSD symptoms and pain following burns. METHODS: This prospective study included 216 patients with burns. PTSD symptoms and pain were measured during hospitalization (T1) and 6 (T2) and 12 months (T3) postburn. The Impact of Event Scale-Revised (IES-R) indexed PTSD symptoms. Acute pain (T1) was the mean pain during the first two weeks of hospitalization measured using an 11-point graphic numeric rating scale. Chronic pain was indexed using the single item 'average' pain from the Brief Pain Inventory (BPI). Catastrophizing was measured at T1 and T2 using the Cognitive Emotion Regulation Questionnaire (CERQ). Data were analysed using structural equation modelling (SEM). RESULTS: The results showed that T2 catastrophizing mediated between acute and chronic PTSD symptoms, and T3 pain. Furthermore, the study revealed significant associations between catastrophizing, PTSD symptoms and pain at the respective measurements, and significant longitudinal associations between the constructs. CONCLUSION: A negative cognitive-affective response to a burn event, such as catastrophizing, mediated the relationship between acute and chronic PTSD symptoms and later chronic pain. Screening for catastrophizing and acute PTSD symptoms is recommended to identify persons at risk for chronic PTSD symptoms and pain. SIGNIFICANCE: The identification of individuals who have the tendency to catastrophize may assist in finding those at risk for development of both chronic PTSD symptoms and chronic pain. Individuals may benefit from early psychological therapy focussing on catastrophizing and acute PTSD symptoms that may ameliorate both chronic PTSD symptoms and pain.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".