Catastrophic thinking about pain as a predictor of length of hospital stay after total knee arthroplasty: a prospective study
Bibliographic record
Abstract
This study prospectively investigates whether catastrophizing thinking is associated with length of hospital stay after total knee arthroplasty. Forty-three patients who underwent primary total knee arthroplasty were included in this study. Prior to their operation all patients were asked to complete the pain catastrophizing scale, and a Western Ontario McMaster Universities Osteoarthritis index. A multiple regression analysis identified pain catastrophizing thinking and age as predictors of hospital stay after total knee arthroplasty. Patients with a higher degree of pain catastrophizing prior to the total knee arthroplasty and those with a higher age have a significantly greater risk for a longer hospital stay. Therefore, the results of this study indicate that the pre-operative level of pain catastrophizing in patients determine, in combination with other variables, the length and inter-individual variation in hospital stay after total knee arthroplasty. Reducing catastrophizing thinking about pain through cognitive-behavioral techniques is likely to reduce levels of fear after total knee arthroplasty. As a result, pain and function immediately post-operative might improve, leading to a decrease in length of hospital stay. Although during the last decades the duration of hospital stay is significantly reduced, this study shows that this can be improved when taking into account the contribution of psychological factors such as pain catastrophizing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".