Patient Knowledge and Beliefs About Knee Osteoarthritis After Anterior Cruciate Ligament Injury and Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To explore patients' knowledge and beliefs about osteoarthritis (OA) and OA risk following anterior cruciate ligament (ACL) injury, to explore the extent to which information about these risks is provided by health professionals, and to examine associations among participant characteristics, knowledge, and risk beliefs and health professional advice. METHODS: A custom-designed survey was conducted in Australian and American adults who sustained an ACL injury, with or without reconstruction, 1-5 years prior. The survey comprised 3 sections: participant characteristics, knowledge about OA and OA risk, and health professional advice. RESULTS: Complete data sets from 233 eligible respondents were analyzed. Most (70%, n = 164) rated themselves as being at greater risk of OA than their healthy peers, although only 56% (n = 130) were able to identify the correct OA definition. While most agreed that ACL (73%, n = 168) and/or meniscal injuries (n = 181, 78%) increase the risk of OA, 65% (n = 152) believed that ACL reconstruction reduced the risk of OA, or they did not know. A total of 27% (n = 62) recalled discussing their OA risk with a health professional. Participants who were female, younger, or had a lower body mass index or higher physical activity level were more likely to recognize meniscal tears and meniscectomy as risk factors of OA. A history of professional advice was associated with beliefs about increased OA risks. CONCLUSION: Patients sustaining an ACL injury require better education from health professionals about OA as a disease entity and their elevated risk of OA, irrespective of whether or not they undergo surgical reconstruction.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".