Three groups of dissatisfied patients exist after total knee arthroplasty: early, persistent, and late
Bibliographic record
Abstract
Aims The primary aim of this study was to assess whether patient satisfaction one year after total knee arthroplasty (TKA) changed with longer follow-up. The secondary aims were to identify predictors of satisfaction at one year, persistence of patient dissatisfaction, and late onset dissatisfaction in patients that were originally satisfied at one year. Patients and Methods A retrospective cohort consisting of 1369 patients undergoing a primary TKA for osteoarthritis that had not undergone revision were identified from an established arthroplasty database. Patient demographics, comorbidities, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, and Short Form 12 (SF-12) questionnaire scores were collected preoperatively, and one and five years postoperatively. In addition, patient satisfaction was assessed at one and five years postoperatively. Logistic regression analysis was used to identify independent predictors of satisfaction at one and five years. Results The overall rate of satisfaction did not change from one (91.7%, n = 1255) to five (90.1%, n = 1234) years (p = 0.16). Approximately half (n = 53/114) of the patients who were dissatisfied at one year became satisfied with their TKA at five years, whereas 6% (n = 74/1255) of those who were satisfied at one year became dissatisfied at five years. At one year, patients with lung disease (p = 0.04), with depression (p = 0.001), with back pain (p < 0.001), undergoing unilateral TKA (p = 0.001), or with a worse preoperative WOMAC pain score (p = 0.04) were more likely to be dissatisfied. Patients with gastric ulceration (p = 0.04) and a worse WOMAC stiffness score (p = 0.047) were at increased risk of persistent dissatisfaction at five years. In contrast, a worse WOMAC pain score (p = 0.01) at one year was a predictor of dissatisfaction in previously satisfied patients at five years. Conclusion Three groups of dissatisfied patients exist after TKA: ‘early’ dissatisfaction at one year, ‘persistent’ dissatisfaction with longer follow-up, and ‘late’ dissatisfaction developing in previously satisfied patients at one year. All three groups have different independent predictors of satisfaction, and potentially addressing risk factors specific to these groups may improve patient outcome and their satisfaction. Cite this article: Bone Joint J 2018;100-B:161–9.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".