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 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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".