Preferences for Posttraumatic Osteoarthritis Prevention Strategies in Individuals With Anterior Cruciate Ligament Injury
Bibliographic record
Abstract
OBJECTIVE: There is growing interest in evaluating new strategies to delay or prevent posttraumatic osteoarthritis (PTOA) in individuals who have sustained anterior cruciate ligament (ACL) injury. This study sought to determine characteristics of potential treatments that are acceptable to patients with ACL injury. METHODS: Participants with a history of ACL injury were recruited from Reddit, Facebook, and ResearchMatch.org. After consent and eligibility confirmation, participants completed a survey comprising questions on (1) demographics, (2) PTOA perceptions, (3) perceived PTOA risk, and (4) a discrete choice experiment (DCE) task. The DCE assessed treatment attributes including risk reduction, side effects, benefits, and out-of-pocket costs. In several scenarios, participants chose between two hypothetical treatments with various attributes or no treatment. The data were analyzed with multinomial logit, mixed logit, and latent class models. RESULTS: ). Of these, 29% experienced daily knee pain, and 35% reported being very or extremely worried about knee OA. The two most influential attributes affecting treatment acceptability were monthly cost and potential mild side effects. Two preference phenotypes emerged: Class 1 members (n = 162, 59%) generally favored treatment, prioritizing effectiveness and injections but were deterred by high cost. Class 2 members (n = 111, 41%) were less inclined to use treatments with potential mild side effects and high cost. CONCLUSION: These results can be used to develop tailored recruitment messaging for future trials. Messaging should emphasize how to manage side effects and out-of-pocket costs.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".