“You’re Perfect for the Procedure! Why Don’t You Want It?” Elderly Arthritis Patients’ Unwillingness to Consider Total Joint Arthroplasty Surgery: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: To explore the process by which elderly persons make decisions about a surgical treatment, total joint arthroplasty (TJA). METHODS: In-depth interviews with 17 elderly individuals identified as potential candidates for TJA who were unwilling to undergo the procedure. RESULTS: For the majority of participants, decision making involved ongoing deliberation of the surgical option, often resulting in a deferral of the treatment decision. Three assumptions may constrain elderly persons from making a decision about surgery. First, some participants viewed osteoarthritis not as a disease but as a normal part of aging. Second, despite being candidates for TJA according to medical criteria, many participants believed candidacy required a level of pain and disability higher than their current level. Third, some participants believed that if they either required or would benefit from TJA, their physicians would advise surgery. CONCLUSION: These assumptions may limit the possibility for shared decision making. CLINICAL IMPLICATIONS: Emphasis should be directed toward thinking about ways in which discussions about TJA might be initiated (and by whom) and considering how patients' views on and knowledge of osteoarthritis in general might be addressed.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".