Specific knowledge and resilience affect short-term outcome in patients following primary total hip arthroplasty
Bibliographic record
Abstract
Abstract Purpose The aim of the present study was to investigate the potential associations between specific knowledge, resilience and patient-reported outcome measures (PROMS) following primary total hip arthroplasty (THA). Methods In a cross-sectional prospective study, consecutive patients following primary THA were included at a rehabilitation center. A novel knowledge score and the validated Connor Davidson Resilience Scale (CD-RISC) were utilized to assess patients’ specific knowledge and resilience, respectively. Additionally, patients completed a qualitative questionnaire regarding the information they had received. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), as well as the University of California and Los Angeles Score (UCLA) served as primary outcome measures. Stepwise multiple regression analysis was performed to identify potential predictors of outcome. Results A total of 103 patients at a mean age of 67.5 years (SD 10.5, 38–88) were included in the analysis at a median of 55.5 days (IQR 43–81) following primary THA. The mean knowledge and resilience scores were 3.8 (SD 1.6, 0–7) and 69.5 (SD 18.5, 0–100), respectively. Forty-seven percent of patients were afraid of harming their prosthesis and these patients had up to 59% worse WOMAC scores (p < 0.001). WOMAC scores on admission to rehabilitation were predicted by resilience and knowledge scores (R2 = 0.106, p = 0.036). UCLA scores at the time of admission were predicted by knowledge scores (R2 = 0.078, p = 0.007). Conclusion The present study demonstrated that patients with a feeling of uncertainty had an inferior short-term functional outcome following primary THA. Moreover, it could be shown that higher specific knowledge and resilience are associated with a better functional outcome according to validated PROMS. While these findings need to be prospectively validated in future studies, specific patient knowledge and resilience may have a direct impact on the outcome of primary THA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".