Development of the Radiographic Union Score for Tibial Fractures for the Assessment of Tibial Fracture Healing After Intramedullary Fixation
Bibliographic record
Abstract
BACKGROUND: : The objective was to evaluate the newly developed Radiographic Union Score for Tibial fractures (RUST). Because there is no "gold standard," it was hypothesized that the RUST score would provide substantial improvements compared with previous scores presented in the literature. METHODS: : Forty-five sets of X-rays of tibial shaft fractures treated with intramedullary fixation were selected. Seven orthopedic reviewers independently scored bony union using RUST. Radiographs were reassessed at 9 weeks. Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI) measured agreement. RESULTS: : Overall agreement was substantial (ICC, 0.86; 95% CI, 0.79-0.91). There was improved reliability among traumatologists compared with others (ICC = 0.86, 0.81, and 0.83, respectively). Overall intraobserver reliability was also substantial (ICC, 0.88; 95% CI, 0.80-0.96). CONCLUSIONS: : The RUST score exhibits substantial improvements in reliability from previously published scores and produces equally reproducible results among a variety of orthopedic specialties and experience levels. Because no "gold standards" currently exist against which RUST can be compared, this study provides only the initial step in the score's full validation for use in a clinical context.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".