Factors Related to Expectations in Individuals Waiting for Total Knee Arthroplasty
Bibliographic record
Abstract
Purpose: There is no consensus on how age and expectations influence planning for total knee arthroplasty (TKA). This study developed and evaluated a new expectation questionnaire and assessed the relationship between preoperative expectations and patient characteristics. Method: The questionnaire evaluated expectations for mobility, pain, participation, and rate of recovery after surgery. Fifty-five participants completed a 6-minute walk test and expectation questionnaire prior to TKA; 17 participants repeated the questionnaire one week later for reliability testing. Analysis of the questionnaire included intra-class correlation coefficient (ICC), homoscedasticity, skewness, kurtosis, multicollinearity, and descriptive measures. A four-step hierarchical linear regression was completed to determine the relationship of patient age, BMI, previous contralateral TKA, and 6-minute walk test scores to expectations. Results: = 0.017). Conclusions: This questionnaire reliably measures patient expectations before TKA; however, further research is needed. Although we anticipated younger age to be related to higher expectations, higher function prior to TKA appears to be more strongly associated with higher expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".