Arthritis symptoms, information sources, and a constantly shifting threshold of risk-benefit ratios influenced elderly patients’ decisions about total joint replacement
Bibliographic record
Abstract
Clark JP, Hudak PL, Hawker GA, et al . The moving target: a qualitative study of elderly patients’ decision-making regarding total joint replacement surgery. J Bone Joint Surg Am 2004;86–A:1366–74.[OpenUrl][1] Q What are the decision making processes of elderly patients with severe arthritis who are unwilling to consider total joint replacement (TJR) surgery? Qualitative. Toronto, Ontario, Canada. 17 patients (age range 59–81 y, 53% women) who had severe arthritis (confirmed by Western Ontario and McMaster Universities Osteoarthritis Index scores ⩾39 out of 100 points and x rays), and were unwilling to consider surgery. Patients were interviewed for a mean 2.5 hours using a semistructured interview guide to elicit the sources and nature of information they received about TJR and potential sources of support; and the preferences, motivation, and needs that were important when considering the general management of arthritis and TJR. Transcribed interview data were analysed using qualitative content analysis. 3 themes described patients’ decision making processes. (1) Factors influencing decisions. Patients differed in terms of the relative importance they placed on … [1]: {openurl}?query=rft.jtitle%253DJ%2BBone%2BJoint%2BSurg%2BAm%26rft.volume%253D86%2526ndash%253BA%26rft.spage%253D1366%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".