Dissociation and Pain-Catastrophizing: Absorptive Detachment as a Higher-Order Factor in Control of Pain-Related Fearful Anticipations Prior to Total Knee Arthroplasty (TKA)
Bibliographic record
Abstract
Total Knee Arthroplasty (TKA) is the ultima-ratio therapy for knee-osteoarthritis (OA), which is a paradigmatic condition of chronic pain. A hierarchical organization may explain the reported covariation of pain-catastrophizing (PC) and dissociation, which is a trauma-related psychopathology. This study tests the hypotheses of an overlap and hierarchical organization of the two constructs, PC and dissociation, respectively, using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Childhood Trauma Screener (CTS), a shortened version of the Dissociative Experiences Scale (FDS-20), the Brief Symptom Inventory (BSI-18), the Pain-Catastrophizing Scale (PCS), and the Tampa Scale of Kinesiophobia (TSK) in 93 participants with knee-OA and TKA. Non-parametric correlation, linear regression, and an exploratory factor analysis comprising the PCS and the FDS-20 in aggregate were run. The three factors: 1) PC factor, 2) absorptive detachment, and 3) conversion altogether explained 60% of the variance of the two scales. Dissociative factors were related to childhood trauma, and the PC-factor to knee-pain. The latter was predicted by absorptive detachment, i.e., disrupted perception interfering with the integration of trauma-related experiences possibly including invasive surgery. Absorptive detachment represents negative affectivity and is in control of pain-related anxieties (including PC). The clinical associations of trauma, psychopathology, and maladaptation after TKA may be reflections of this latent hierarchical organization of trauma-related dissociation and PC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".