Activity outcomes after hip arthroplasty: an information tool based on patients’ experience captured in a hospital registry
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients receiving total hip arthroplasty (THA) have different expectations and concerns about their health outcomes after surgery. In this study we developed a tool based on registry data to inform patients and their clinicians about activity outcomes after THA. METHODS: We used data from the Geneva Arthroplasty Registry (GAR) on patients receiving a primary elective THA between 1996 and 2019. The information tool was developed around five activity outcomes: getting in/out of the car, getting dressed autonomously, independence in weekly tasks, interference in social activities, and activity levels. Based on baseline predictors, conditional inference trees (CITs) were used to create clusters of patients with homogeneous activity outcomes at one, five and 10 years after surgery, rather than to predict individual probabilities. RESULTS: In total, 14 CITs were generated based on 6,836 operations included in the tool. Overall, activity outcomes substantially improved at all three times points after surgery, with 1-year values mostly being the highest. While before surgery only about 10% of patients had none/slight limitations in activities of daily living, about 70% did one year after surgery. The SF12 mental component score (MCS), SF12 self-rated health (SRH), BMI, ASA score, and comorbidity count were the most recurring predictors of activity outcomes. Predictors and their relative importance changed at different time points for the same outcome. For example, for ability to get in/out the car, whilst clusters at year 1 were generated based on WOMAC function, SRH, mental health, WOMAC difficulty walking, and SF12 physical interference, at year 5, ASA score, BMI, SF12 physical & mental health, activity level, and socio-economic status were significant. Outcome profiles varied by clusters. CONCLUSION: Distinct activity outcomes clusters based on baseline patient characteristics were identified and knowing this can help inform patients' expectation and meaningful discussions with clinicians about treatment decisions.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".