Reliability of assessing proximal femur geometry with Rutz classification schema in patients with cerebral palsy
Bibliographic record
Abstract
Our investigation aimed to assess the reliability of the femoral head shape classification system devised by Rutz et al . and observe its application in patients with cerebral palsy (CP) at different skeletal maturity levels. Four independent observers assessed anteroposterior radiographs of the hips of 60 patients with hip dysplasia associated with non-ambulatory CP (Gross Motor Function Classification System levels IV and V) and recorded the femoral head shape radiological grading system as described by Rutz et al . Radiographs were obtained from 20 patients in each of three age groups: under 8 years, between 8 and 12 years and above 12 years old, respectively. Inter-observer reliability was assessed by comparing the measurements of four different observers. To determine the intra-observer reliability, radiographs were reassessed after a 4-week interval. Accuracy was checked by comparing these measurements with the assessment of expert consensus. Validity was checked indirectly by observing the relationship between the Rutz grade and the migration percentage. The Rutz classification system's evaluation of femoral head shape showed moderate to substantial intra- and inter-observer reliability (mean κ = 0.64 for intraobserver and mean κ = 0.5 for interobserver). Specialist assessors had slightly higher intra-observer reliability than trainee assessors. The grade of femoral head shape was significantly associated with increasing migration percentage. Rutz's classification was shown to be reliable. Once the clinical utility of this classification can be established, it has the potential for broad application for prognostication and surgical decision-making and as an essential radiographic variable in studies involving the outcomes of hip displacement in CP. Level of evidence: III.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".