Does patient perception of alignment affect total knee arthroplasty outcome?
Bibliographic record
Abstract
OBJECTIVE: This study was designed to address a recurring observation in our centre that, despite a satisfactory postoperative radiographic limb alignment, some patients are dissatisfied with the alignment and appearance of their operated leg. We carried out a prospective survey to determine patient perception of limb alignment after total knee arthroplasty (TKA) and whether level of satisfaction with alignment affects clinical outcome. METHODS: Patients self-rated their alignment, their satisfaction with alignment and their level of knee pain on a visual analogue scale (VAS). Additional outcome measures included pre- and postoperative Knee Society Score (KSS), Oxford Knee Score (OKS) and the Health Survey Short Form (SF-12). RESULTS: Twenty of 87 (23%) patients were dissatisfied with their new leg alignment and had a poorer perception of pain and range of motion after TKA. Despite this finding, KSS and OKS were no different between patients who were satisfied and those who were dissatisfied with their limb alignment. The SF-12 showed a trend toward lower scores in patients who were dissatisfied with their limb alignment. CONCLUSIONS: Satisfaction with perceived limb alignment appears to influence outcome after TKA and is not reflected in current outcome scales. Perhaps patients should be counselled on how alignment is restored and on what to expect of their limb alignment and appearance after TKA.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".