When Has a Knee or Hip Replacement Failed? A Patient Perspective
Bibliographic record
Abstract
OBJECTIVE: To define the patient perspective of what constitutes a failure of total joint replacement (TJR) in a qualitative study. METHODS: We used the nominal group technique (NGT) with participants who had undergone elective total hip replacements (THR) and/or total knee replacements (TKR) to answer the question, "When would you consider a knee or hip replacement to be a failure?" RESULTS: We performed 8 nominal groups with 42 participants, all of whom had undergone THR and/or TKR between 2016 and 2018. Of these, 48% were male, 17% were Black, 79% had college education or above, and 76% had had osteoarthritis as the underlying diagnosis. The nominated responses/themes that were ranked the highest by the participants were as follows: (1) refractory index joint pain (80 votes); (2) occurrence of postoperative adverse events (54 votes); (3) unable to resume normal activities or go back to work (38 votes); (4) little or no improvement in quality of life (35 votes); (5) early revision surgery (35 votes); (6) death (7 votes); and (7) other, including nurse or physician negligence (2 votes) and expectation-outcome mismatch (1 vote). CONCLUSION: Lack of relief of pain or restoration of function or quality of life, or the occurrence of surgical complications after TJR were defined as TJR failure by participants. Functional TJR failure seems as important or more important than surgical failure. This patient perspective emphasizing pain, function, satisfaction, adverse events, and revision as critical domain components of TJR failure independently validated their inclusion in the TJR core domain set for clinical trials in people undergoing knee or hip TJR.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".