Dimensions of catastrophic thinking associated with pain experience and disability in patients with neuropathic pain conditions
Bibliographic record
Abstract
The objective of the present study was to examine the relative contributions of different dimensions of catastrophic thinking (i.e. rumination, magnification, helplessness) to the pain experience and disability associated with neuropathic pain. Eighty patients with diabetic neuropathy, post-herpetic neuralgia, post-surgical or post-traumatic neuropathic pain who had volunteered for participation in a clinical trial formed the basis of the present analyses. Spontaneous pain was assessed with the sensory and affective subscales of the McGill Pain Questionnaire. Pinprick hyperalgesia and dynamic tactile allodynia were used as measures of evoked pain. Consistent with previous research, individuals who scored higher on a measure of catastrophic thinking (Pain Catastrophizing Scale; PCS) also rated their pain as more intense, and rated themselves to be more disabled due to their pain. Follow up analyses revealed that the PCS was significantly correlated with the affective subscale of the MPQ but not with the sensory subscale. The helplessness subscale of the PCS was the only dimension of catastrophizing to contribute significant unique variance to the prediction of pain. The PCS was not significantly correlated with measures of evoked pain. Catastrophizing predicted pain-related disability over and above the variance accounted for by pain severity. The findings are discussed in terms of mechanisms linking catastrophic thinking to pain experience. Treatment implications are addressed.
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 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".