Field testing the Unified Classification System for periprosthetic fractures of the femur, tibia and patella in association with knee replacement
Bibliographic record
Abstract
The Unified Classification System (UCS) was introduced because of a growing need to have a standardised universal classification system of periprosthetic fractures. It combines and simplifies many existing classification systems, and can be applied to any fracture around any partial or total joint replacement occurring during or after operation. Our goal was to assess the inter- and intra-observer reliability of the UCS in association with knee replacement when classifying fractures affecting one or more of the femur, tibia or patella. We used an international panel of ten orthopaedic surgeons with subspecialty fellowship training and expertise in adult hip and knee reconstruction ('experts') and ten residents of orthopaedic surgery in the last two years of training ('pre-experts'). They each received 15 radiographs for evaluation. After six weeks they evaluated the same radiographs again but in a different order. The reliability was assessed using the Kappa and weighted Kappa values. The Kappa values for inter-observer reliability for the experts and the pre-experts were 0.741 (95% confidence interval (CI) 0.707 to 0.774) and 0.765 (95% CI 0.733 to 0.797), respectively. The weighted Kappa values for intra-observer reliability for the experts and pre-experts were 0.898 (95% CI 0.846 to 0.950) and 0.878 (95% CI 0.815 to 0.942) respectively. The UCS has substantial inter-observer reliability and 'near perfect' intra-observer reliability when used for periprosthetic fractures in association with knee replacement in the hands of experienced and inexperienced users.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".