Expectations of recovery from revision knee replacement
Bibliographic record
Abstract
OBJECTIVE: To evaluate outcome expectations of patients undergoing revision total knee replacement (TKR) and to examine personal factors, patient functioning, previous experiences with knee replacement surgery, concerns about surgery, and general health as predictors of expectations. METHODS: Revision TKR patients (n = 184, 54% women; mean age 69 years) completed a questionnaire up to 2 weeks before surgery. This included demographics, experience with previous knee surgery, concerns about surgery, the Life Orientation Test (LOT), the Arthritis Helplessness Scale, the Western Ontario and McMaster Universities Osteoarthritis Index, and a rating of overall health. Outcome expectations were evaluated as 5 questions assessing global benefit; relief of pain; ease of disability; expectations of having complications; and whether the person expected to be fully recovered from surgery in <6 months, 6-12 months, >12 months, or did not expect to recover. Predictors of each of the 5 outcome expectations were evaluated using univariable and multivariable regression analyses. RESULTS: Expectations are a multidimensional construct (Cronbach's alpha = 0.63). Expectation of global benefit of surgery was high, but was lower for benefits related to ease of pain and improved function. Concerns about surgery were a consistent predictor of all expectation outcomes in multivariable modeling. When concerns about surgery and general health were entered into the model as an interaction with expectation of recovery time as the outcome, past experience (P = 0.05), pain (P = 0.03), LOT (P = 0.03), and interaction between concerns about surgery and general health were significant predictors. CONCLUSION: Clinicians need to understand and help patients shape appropriate expectations for recovery from revision TKR.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".