Can Fluoroscopy-based Computer Navigation Improve Entry Point Selection for Intramedullary Nailing of Femur Fractures?
Bibliographic record
Abstract
BACKGROUND: The entry point is crucial to an accurate reduction in femoral nailing. Fluoroscopy-based navigation was developed to aid in reducing femur fractures and selecting entry points. QUESTIONS/PURPOSES: We asked: (1) Can the piriformis fossa (PF) and tip of the greater trochanter (TT) be identified with high reproducibility? (2) What is the range of nonneutral images clinically acceptable for entry point selection? (3) Does navigation improve accuracy and precision of landmarking the TT and PF? And (4) does off-angle fluoroscopy within the acceptable range affect landmark accuracy? METHODS: Three orthopaedic surgeons digitized the PF and TT under direct visualization on 10 cadaveric femurs, quantifying the reproducibility of the targeted PF and TT landmarks. Arcs of acceptable AP and lateral images of each femur were acquired in increments of 5° with a C-arm. An experienced orthopaedic surgeon rejected or accepted images for entry point selection by qualitatively assessing the relative positions and sizes of the greater trochanter, lesser trochanter, and femoral neck. Entry points were identified on each image using fluoroscopy and navigation. Hierarchical linear modeling was used to compare accuracy and precision between navigation and fluoroscopy and the effects of image angle. RESULTS: A 29° average arc of acceptable images was found. Reproducibility of the target landmarks for the PF and TT under direct visualization was excellent. Navigation had similar accuracy to fluoroscopy for PF localization but less for TT. Navigation increased precision compared to fluoroscopy for both PF and TT. Image angle affected accuracy of the PF and TT under fluoroscopy and navigation. CONCLUSIONS: Nonorthogonal images reduce accuracy of PF and TT identification with both navigation and fluoroscopy. Navigation increased precision but decreased accuracy and cannot overcome inaccuracies induced by nonorthogonal images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".