Early Clinically Relevant Improvement in Quality of Life and Clinical Outcomes 1 Year Postsurgery in Patients with Knee and Hip Joint Arthroplasties
Bibliographic record
Abstract
Objective To measure and identify the determinants of the outcomes after hip/knee arthroplasty (HA/KA) in patients with osteoarthritis during the first postsurgical year. Design In this prospective observational study, we evaluated the preoperative and postoperative (3, 6, and 12 months) outcomes of 626 patients who underwent HA (346 with median age 65 years, 59% female) or KA (280 with median age 66.5 years, 54% female) between 2008 and 2013. Generic and specific tools were used to measure health-related quality of life (HRQoL) and utility. Good outcome was defined as an improvement in WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) greater than or equal to the minimal important difference (MID). Regressions were performed to evaluate the relationship between preoperative and postoperative measures and evolution of WOMAC/good outcome. Results We observed an almost systematic improvement of all parameters for up to 12 months, but especially at the 3-month follow-up. The low number of comorbidities and the absence of postoperative complications were the common determinants of improvement of WOMAC total score after 12 months. Other parameters (background of the joint, preoperative function and length of hospital stay in KA group; place of discharge in HA group) affected the evolution of WOMAC scores. 87.09% of HA and 73.06% of KA patients experienced a good outcome. A small number of comorbidities, a worse preoperative function, a shortened hospital stay (KA only), and an absence of early postoperative complications (HA only) significantly predicted a good outcome. Conclusions Intermediate HRQoL following HA or KA improved quickly from preoperative levels for all instruments. More than 70% of patients achieved a good outcome defined as improved pain, stiffness and disability and the predictors are slightly close.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".