Functional improvement after Total Knee Arthroplasty Revision: New observations on the dimensional nature of outcome
Bibliographic record
Abstract
BACKGROUND: Despite the numerous outcomes measures described it remains unclear what aspects of patient outcome are important in determining actual improvement following total knee arthroplasty revisions (TKAR). We performed a prospective cohort study of TKAR to determine the components of clinical improvement and how they are related and best measured. METHODS: An improvement scale was devised utilizing data from 186 consecutive TKAR patients on SF-36 physical (PCS) and mental (MCS) components, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) Index, Knee Society Score (KSS), a novel Activity Scale (AS) and a physician derived severity assessment scale performed both preoperatively and at 6 month post-operative follow-up. The change in each of these scores was analyzed using factor analysis, deriving a composite improvement scale. RESULTS: All the instruments demonstrated statistically significantly better scores following TKAR (except the SF-36 MCS). Furthermore, all significant correlations between the scores were positive. Statistical factor analysis demonstrated that scores could be arranged into 4 related factor groupings with high internal consistency (Cronbach Alpha = 0.7). Factor 1 reflected patient perceived functional outcomes, Factor 2 activity levels, Factor 3 the MCS and Factor 4 the KSS. CONCLUSION: This study demonstrates that improvement following TKAR has a multidimensional structure. The improvement scales represent a more coordinated method of the previously fragmented analysis of TKAR outcomes. This will improve assessment of the actual effectiveness of TKAR for patients and what aspects of improvement are most critical.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".