178 Does Physical Activity Change Following Hip and Knee Replacement? An Analysis of Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
Background: Total hip (THR) and knee replacement (TKR) procedures aim to reduce debilitation associated with end-stage OA to increase quality of life and physical performance. While it is assumed that this would automatically translate into increased engagement in physical activity, this has been recently questioned. The purpose of this analysis was to determine whether the type and level of physical activity increases during the initial 24 post-operative months compared with pre-operative levels, and how this change compares with people without arthroplasty or OA. Methods: This study was an analysis of a North American prospective cohort dataset [Osteoarthritis Initiative (OAI)] of community-dwelling individuals. Data from all people who had undergone a THR or TKR with a minimum of baseline and 24 month follow-up data were identified. These were compared with data from people who had not undergone a THR or TKR and who did not have a diagnosis of hip or knee OA during the OAI follow-up period. Data collected included demographic characteristics, medical morbidities, musculoskeletal health and physical activity/active living measures of function reported pre-THR/TKR and then at the 12 and 24 month follow-up data collection intervals. Data were analysed using interferential statistical tests to compare within-individual and between-group differences in physical activity correlated during each follow-up interval.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".