Femorotibial angle scan–rescan reproducibility: A high‐precision calculation on a large cohort
Bibliographic record
Abstract
Abstract Purpose Femorotibial angle (FTA) is a convenient measure of coronal knee alignment that can be extracted from a short knee radiograph, avoiding the additional radiation exposure and specialist equipment required for full‐leg radiographs. While intra‐ and inter‐reader reproducibility from the same image has been reported, the full scan–rescan reproducibility across images, as calculated in this study, has not. Methods In this study, 4589 FTA measurement pairs from 2586 subjects acquired a year apart were used to estimate FTA scan–rescan reproducibility using data from the Osteoarthritis Initiative. Subjects with radiographic progression of osteoarthritis or other conditions that may cause a change in coronal knee alignment were excluded. Measurement pairs were analysed using paired‐samples tests to detect differences and compared to symptomatic changes in Western Ontario and McMaster Universities Arthritis Index scores for joint pain, stiffness and physical function to detect correlations. Results The 95% limit of agreement and the paired‐samples correlation were calculated with high precision to be [−1.76°, +1.78°] and 0.938, considerably worse than the corresponding figures for intra‐ and inter‐reader reproducibility, without relation to symptomatic or radiographic changes in knee condition. This error will weakly attenuate and values from their true values in correlative studies involving FTA. The realistic maximum value for is 87% and for Pearson's is 93%. Conclusion The scan–rescan reproducibility in FTA is almost double the intra‐ and inter‐reader reliability from a single scan. At almost ±2° accuracy, FTA is inappropriate for surgical use, but it is sufficiently reproducible to produce good correlations in studies predicting disease incidence and progression. Level of Evidence Level II, retrospective study.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".