The well-being of people with anterior cruciate ligament rupture-related post-traumatic osteoarthritis in Aotearoa New Zealand
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament (ACL) ruptures are a potent risk factor for post-traumatic knee osteoarthritis (PTOA). Annually, in Aotearoa New Zealand, approximately 2,500 people under the age of 30 undergo ACL reconstruction surgery. Due to the young age of injury and surgery, many develop osteoarthritis before age 50 and have a higher likelihood of requiring total knee replacement compared to the general population. This study aimed to gain insight into the medium- to long-term impacts of ACL rupture on people's well-being in Aotearoa New Zealand, by exploring their lived experiences five or more years post-injury. METHOD: In this Interpretive Description observational study, we conducted semi-structured interviews with people who had ruptured their ACL and had or were at risk of developing PTOA. Analysis was conducted guided by Braun and Clarke's Reflexive Thematic Analysis. FINDINGS: Twelve people (7 women, median age 49.5 [25-62] years) were interviewed. Three themes were generated from the data: 1) Nobody Ever Told Me…, 2) The Post-Rehabilitation Void, and 3) The Elephant in the Room: The Psychosocial Impact. Participants commonly described fear, grief and long-term psychological impacts, and most reported wanting to know more about the long-term management of their knees. CONCLUSION AND IMPACT: The study highlights opportunities to provide better long-term support and management, improve outcomes, and reduce the burden on these individuals. ACL injury can profoundly impact people's lives in the long term. Better education, support services, and consideration of psychosocial factors are needed. Addressing identified barriers could reduce the individual and socioeconomic burden of PTOA for New Zealanders. Future research involving stakeholders must establish acceptable long-term management programmes tailored to ensure they meet the population's needs and address the unique socioeconomic context and ethnic disparities in Aotearoa New Zealand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".