One report, multiple aims: orthopedic surgeons vary how they use patient-reported outcomes with patients
Bibliographic record
Abstract
PURPOSE: We conducted semi-structured qualitative interviews with surgeons to assess their goals for incorporating a patient-reported outcome measure (PROM)-based shared decision report into discussions around surgical and non-surgical treatment options for osteoarthritis of the knee and hip. METHODS: Surgeons actively enrolling patients into a study incorporating a standardized PROM-based shared decision report were invited to participate in a semi-structured interview lasting 30 min. Open-ended questions explored how the surgeon used report content, features that were helpful, confusing, or could be improved, and how use of the report fit into the surgeon's workflow. We used a conventional content analysis approach. RESULTS: Of the 16 eligible surgeons, 11 agreed to participate with 9 completing the interview and 2 withdrawing due to work demands. We identified 8 themes related to PROM-based report use: Acceptability, Patient Characteristics, Communication Goals, Useful Content, Not Useful Content, Challenges, Training Needs, and Recommended Improvements. Additional sub-themes emerged for Communication Goals (7) and Challenges (8). All surgeons shared positive feedback about using the report as part of clinical care. Whereas surgeons described the use of the report to achieve different goals, the most common uses related to setting expectations for post-surgical outcomes (89%) and educating patients (100%). CONCLUSION: Surgeons tailor their use of a PROM-based report with individual patients to achieve a range of aims. This study suggests multiple opportunities to further our understanding of the ways PROMs can be used in clinical practice. The way PROM information is visually displayed and multi-component reports are assembled can facilitate diverse aims.
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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 | 0.084 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".