Assessment of Radiographic Fracture Healing in Patients With Operatively Treated Femoral Neck Fractures
Bibliographic record
Abstract
OBJECTIVES: This study was conducted to determine interrater and intrarater reliabilities on the healing assessment of femoral neck fractures between orthopedic surgeons and radiologists and to test the performance of a checklist system for hip fracture healing. METHODS: We developed and used a scoring system [radiographic union score in hip fracture (RUSH) score] to determine the validity of quantifying fracture healing. A panel of 6 reviewers (3 orthopedic surgeons and 3 radiologists) independently assessed fracture healing with the RUSH system using radiographs of 150 femoral neck fractures at various stages in healing on 2 occasions 4 weeks apart. RESULTS: Using subjective assessment, the interrater agreement between reviewer groups for fracture healing was fair [intraclass coefficient = 0.22, 95% confidence interval (CI): 0.01-0.41] with no significant difference in agreement within the orthopedic surgeon and radiologist groups (0.17 vs. 0.21). There was higher agreement for fracture healing using the RUSH score (intraclass coefficient = 0.53, 95%CI: 0.30-0.69) compared with physician impression of healing, highlighting the difficulties with plain radiographic assessments of healing. Intrarater agreement was consistently high across all measures for both surgeons and radiologists. The RUSH score and medial cortex bridging correlated well with overall assessment of healing (r = 0.868 and 0.643, respectively). CONCLUSIONS: The level of agreement between and within orthopedic surgeon and radiologist reviewers in the assessment of fracture healing is low, though intrarater agreement is high. The RUSH score shows promise as a tool to improve agreement on fracture healing. Studies evaluating reliability and accuracy of healing with clinical information and temporal evaluation are needed and may further improve agreement.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".