- Design
- Retrospective multicentre derivation study
- Population
- 1012 adults aged 18–65 with femoral neck fractures fixed with three cannulated screws
- Primary outcome
- Osteonecrosis of the femoral head, minimum 2-year follow-up
- Effect
- 4% (score 0–3) vs 42% (score 6–8); AUC 0.80 vs 0.70 for Garden
Three Chinese centres reviewed 1012 adults aged 18 to 65 whose femoral neck fractures were fixed with three cannulated screws between 2012 and 2023; 21% developed osteonecrosis of the femoral head.
Five preoperative features made up the score: age 45 or over (1 point), displacement (3), comminution (1), a superior subcapital fracture line (2) and an inferior subcapital line (1). Osteonecrosis occurred in 4% of patients scoring 0 to 3 and 42% of those scoring 6 to 8. The score discriminated better than the Garden classification (AUC 0.80 vs 0.70). A score below 4 had a negative predictive value of 96%.
It uses information already on the injury radiographs, so it can be applied at the first consultation. It helps counselling: a high-scoring 50-year-old should know that nearly half of similar patients developed osteonecrosis. It has not been validated outside these centres, and a fifth of patients were excluded for missing data.
- Score every young femoral neck fracture from the injury films before deciding on fixation.
- For scores 6–8, discuss the roughly 40% osteonecrosis risk and the arthroplasty alternative where age allows.
- For scores 0–3, fixation carries a low osteonecrosis risk — say so.
- Plan follow-up imaging for at least two years in intermediate and high scorers.
Why it matters
It gives a young patient a number for the complication most likely to turn fixation into a hip replacement.
Don't overread it
The score is internally derived and not externally validated; use it for counselling, not to make the operative decision on its own.
The statistics, in plain English
An AUC of 0.80 means that, given one patient who developed osteonecrosis and one who did not, the score ranks them correctly about 80% of the time. Scores built and tested on the same data usually perform worse elsewhere, so expect the real-world discrimination to be lower.
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