Limiting the Risk of Osteoarthritis After Anterior Cruciate Ligament Injury: Are Health Care Providers Missing the Opportunity to Intervene?
Bibliographic record
Abstract
OBJECTIVE: To understand what sports orthopedic surgeons (OS), primary care physicians (PCPs) with sports medicine training, and physical therapists (PTs) managing nonelite athletes with anterior cruciate ligament (ACL) injury tell their patients about their osteoarthritis (OA) risk. METHODS: An electronic survey was distributed by the Canadian Academy of Sport and Exercise Medicine (PCPs, OS), the Sports and Orthopedic Divisions of the Canadian Physiotherapy Association (PTs), and to OS identified through the Royal College of Physicians and Surgeons and the Canadian Orthopaedic Association. The survey included 4 sections: demographics, factors discussed, timing of discussions, and discussion of risk factors and their management. Proportions or means with 95% confidence intervals were calculated. RESULTS: A total of 501 health care professionals (HCPs) responded (98 PCPs, 263 PTs, and 140 OS). Of those responding, 70-77% of physicians reported always discussing OA risk, but only 35% of PTs did. All HCPs reported that patient activities perceived as detrimental to knee health, ACL reinjury, and simultaneous injury to other structures in the knee were most often the reason for discussing OA risk. OA risk was discussed at initial management post-injury (65-94%), with few discussing risk subsequently. Eighty percent of physicians and 99% of PTs indicated that PTs were suited to provide OA risk and management information. CONCLUSION: HCPs routinely managing people with ACL injury do not consistently discuss OA risk post-injury with them. Educational strategies for HCPs are urgently needed to develop care pathways inclusive of support for OA risk management following ACL injury.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".