Traumatic knee injury healthcare pathways and outcomes: the Australian Knee Injury Inception Cohort Study (KIICS) protocol
Bibliographic record
Abstract
Decision-making for the optimal management of traumatic knee injuries can be challenging. Clinical trials reveal only small differences between surgical and non-surgical approaches, while patient and clinician biases, as well as healthcare access issues, may also influence management. Little is known about the real-world healthcare pathways for patients with a knee injury, as well as the person- and/or healthcare-related factors that influence management strategies and outcomes. The Australian Knee Injury Inception Cohort Study (KIICS) aims to: (1) describe healthcare pathways following acute knee injury (including the timing and type of healthcare consultations); (2) identify patient- and/or healthcare-related predictors of management strategy (ie, surgical vs non-surgical); and (3) examine the long-term outcome of different injury types, healthcare pathways and management strategies. KIICS is a nationwide prospective longitudinal inception cohort study recruiting Australians who have sustained an acute knee injury within the previous 6 months that disrupted daily activities or sports and led to a healthcare consultation. Participants will complete online questionnaires at enrolment and at 6 months, 1, 2, 5 and 10 years post-injury. The data to be collected will include sociodemographic characteristics, knee injury history, the sequence of healthcare consultations and referral patterns, and management strategies (ie, surgical vs non-surgical). Patient-reported outcomes will include knee pain and instability, knee-related quality of life, patient-acceptable symptom state, health-related quality of life, mental health, fear of reinjury, return-to-sport status and activity level. Detailed statistical analysis plans will be developed to address the study's key research questions, informing clinical practice, shared decision-making and healthcare policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".