Patient Perceptions of Medication Therapy for Prevention of Posttraumatic Osteoarthritis Following Anterior Cruciate Ligament Injury: A Qualitative Content Analysis
Bibliographic record
Abstract
OBJECTIVE: Posttraumatic osteoarthritis (PTOA) accounts for nearly 12% of osteoarthritis incidences and often occurs after anterior cruciate ligament (ACL) tear. Ensuring the uptake of preventive treatments for PTOA requires that investigators and clinicians understand factors influencing patients to seek preventive therapies. This qualitative, descriptive study aimed to assess individuals' willingness to adopt a medication therapy for PTOA prevention following ACL injury. METHODS: We enrolled participants who had an ACL tear within two years of enrollment. Study individuals participated in a semistructured interview or focus group. We reviewed audio transcriptions for accuracy, and then organized the data inductively, beginning with open coding of audio transcriptions using NVivo 12. Finally, using a qualitative content analysis approach, we identified, revised, and constructed themes and subthemes. RESULTS: Twenty-five individuals (mean age 25 years, 60% women) participated. Participants were an average of 10 months after injury (mean 310 days, 95% confidence interval [CI] 249-371) and reported a mean Knee Injury and Osteoarthritis Outcome Score pain score of 80.3 (95% CI 74.5-86.2). We identified three main themes related to general treatment for PTOA (eg, unwanted side effects), medication treatment for PTOA (eg, concern about pill size and dose frequency), and clinical trial attributes (eg, time commitment). CONCLUSION: Although participants expressed great interest in trying medication therapy for PTOA prevention, there was variability in which components of treatment mattered to them. Our results stress the importance of using qualitative approaches such as this one to inform the design of trials and treatments that real-world patients will pursue with enthusiasm.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".