Pain catastrophizing and pre-operative psychological state are predictive of chronic pain after joint arthroplasty of the hip, knee or shoulder: results of a prospective, comparative study at one year follow-up
Bibliographic record
Abstract
PURPOSE: To assess the relationship between pre-operative psychological state, postoperative pain and function one year after total shoulder, hip or knee arthroplasty. METHODS: Patients undergoing shoulder, hip or knee arthroplasty between March 2014 and November 2015 were included. Pain catastrophizing score (PCS) was determined preoperatively, at six months and at one year follow-up. Joint pain at rest was quantified using a Visual Analogue Scale (0‒100). Depressive symptoms were measured using the Beck Depression Inventory or Geriatric Depression Score, situational anxiety and dispositional anxiety were measured using the State-Trait Inventory and joint function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index for the hip and knee and Oxford Shoulder Score for the shoulder. RESULTS: ). Pre-operative PCS was significantly correlated with pain > 30/100 at one year and with functional scores, for all joints. Multivariate analysis identified the following variables as risk factors for pain intensity > 30/100 at one year: pre-operative PCS > 20/52 (OR = 3.5 ± 1.1 [95% CI: 1.9‒6.6]; p = 0.0001), trait-anxiety score ≥ 46 (OR = 2.4 ± 0.9 [95% CI: 1.1‒5.2]; p = 0.03), pre-operative pain ≥ 60/100 (OR = 3.2 ± 1 [95% CI: 1.8‒6.1]; p = 0.0001) and pain for at least 3 years (OR = 1.8 ± 0.6 [95% CI: 1‒3.4]; p = 0.04). CONCLUSION: Pre-operative pain catastrophizing and trait-anxiety are risk factors for post-operative pain after shoulder, hip and knee arthroplasty. TRIAL REGISTRATION NUMBER: www. CLINICALTRIALS: gov NCT02361359.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".