Ideal Tibial Intramedullary Nail Insertion Point Varies With Tibial Rotation
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to investigate how superior entry point varies with tibial rotation and to identify landmarks that can be used to identify suitable radiographs for successful intramedullary nail insertion. METHODS: The proximal tibia and knee were imaged for 12 cadaveric limbs undergoing 5° increments of internal and external rotation. Medial and lateral arthrotomies were performed, the ideal superior entry point was identified, and a 2-mm Kirschner wire inserted. A second Kirschner wire was sequentially placed at the 5-mm and then the 10-mm position, both medial and lateral to the initial Kirschner wire. Radiographs of the knee were obtained for all increments. The changing position of the ideal nail insertion point was recorded. RESULTS: A 30° arc (range, 25°-40°) provided a suitable anteroposterior radiograph. On the neutral anteroposterior radiograph, the Kirschner wire was 54% ± 1.5% (range, 51-56%) from the medial edge of the tibial plateau. For every 5° of rotation, the Kirschner wire moved 3% of the plateau width. During external rotation, a misleading medial entry point was obtained. A fibular bisector line correlated with an entry point that was ideal or up to 5 mm lateral to this but never medial. The film that best showed the fibular bisector line was between 0° and 10° of internal rotation of the tibia. CONCLUSIONS: The fibula head bisector line can be used to avoid choosing external rotation views and, thus, avoid medial insertion points. The current results may help the surgeon prevent malalignment during intramedullary nailing in proximal tibial fractures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".