Improving Management of Osteoarthritis: Patients’ Perceptions of a Surgical Readiness Interview Tool
Bibliographic record
Abstract
Almost half of patients referred to orthopedic surgeons for Total Joint Arthroplasty (TJA) do not require TJA at that time or are not appropriate surgical candidates. The objective was to explore patients’ perspectives of a Surgical Readiness Interview Tool and its potential utility in the Osteoarthritis (OA) management process. Semi-structured qualitative interviews were conducted with a convenience sample of patients from arthroplasty clinics in Alberta. All interviews were digitally recorded and transcribed verbatim. Analysis was conducted iteratively, applying a constant comparison method. Nine patients were interviewed. Participants found the interview tool to be relevant and comprehensible. Suggestions were made on how to improve tool clarity and administration processes. Patient orientation versus that of the doctor, and expectation management emerged as salient factors in the meaningful application of the tool. As a result of the interviews, a revised tool was developed which incorporated the participant suggestions. Patients were positive about the interview tool and felt that it would lead to better care provision, particularly with incorporation of participants’ suggestions. The data suggest that the interview tool could improve the conversation on surgical readiness, conservative management, and addressing modifiable risk factors prior to TJA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".