Determining the Reliability of the Graf Classification for Hip Dysplasia
Bibliographic record
Abstract
We sought to establish the levels of interrater reliability and intrarater reliability of the Graf classification among orthopaedic surgeons in their final training year and who learned the method by instructed teaching or self study. Using standard teaching material developed by Graf, two groups of senior orthopaedic residents at the same training level received structured teaching sessions (Group A, n = 2) or performed self study (Group B, n = 2). Interrater reliability and intrarater reliability were determined (Cohen's weighted kappa). Proportions of correctly rated sonograms were compared between groups, implications of misclassifications were analyzed, and sensitivity analyses were performed. Interrater reliability was 0.59 (95% CI = 0.32-0.85) for Group A, and 0.47 (95% CI = 0.14-0.79) for Group B. Intrarater reliability showed an overall kappa of 0.57 (95% CI = 0.35-0.78) in Group A, and 0.47 (95% CI = 0.19-0.75) in Group B. The proportion of correctly rated sonograms between groups was similar in the original dataset and in the sensitivity analysis. Misclassifications influencing treatment were infrequent; one patient would have received unwarranted treatment and three patients would not have received warranted treatment. The Graf classification showed moderate reliability. Using self study, it can be learned almost, but not quite as effectively as by a structured program.
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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 | 0.075 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".