Same knee, different goals: patients and surgeons have different priorities related to ACL reconstruction
Bibliographic record
Abstract
PURPOSE: The priorities of patients should be shared by those treating them. Patients and surgeons are likely to have different priorities surrounding anterior cruciate ligament reconstruction (ACLR), with implications for shared decision-making and patient education. The optimal surgical approach for ACLR is constantly evolving, and the magnitude of treatment effect necessary for evidence to change surgical practice is unknown. The aim of this study was to determine (1) the priorities of surgeons and patients when making decisions regarding ACLR and (2) the magnitude of reduction in ACLR graft failure risk that orthopaedic surgeons require before changing practice. METHODS: This study followed a cross-sectional survey design. Three distinct electronic surveys were administered to pre-operative ACLR patients, post-operative ACLR patients, and orthopaedic surgeons. Patients and surgeons were asked about the importance of various outcomes and considerations pertaining to ACLR. Surgeons were asked scenario-based questions regarding changing practice for ACLR based on new research. RESULTS: Surgeons were more likely to prioritize outcomes related to the surgical knee itself, whereas patients were more likely to prioritize outcomes related to their daily lifestyle and activities. Knee instability and risk of re-injury were unanimous top priorities among all three groups. A mean relative risk reduction in ACLR graft failure of about 50% was required by orthopaedic surgeons to change practice regardless of the type of change, or patient risk profile. CONCLUSION: There are discrepancies between the priorities of surgeons and patients, and orthopaedic surgeons appear resistant to changing practice for ACLR. LEVEL OF EVIDENCE: IV.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".