Optimizing the Entry Point for Medullary Hip Screws
Bibliographic record
Abstract
INTRODUCTION: Medullary hip screws (MHSs) are the most common treatment of intertrochanteric hip fractures because they can be used for varied fracture patterns and resist shortening. Identifying the appropriate MHS entry point can be intellectually and technically challenging. We aimed to quantify the variability in the ideal entry point (IEP) for MHSs. METHODS: Standing alignment radiographs of 50 patients were evaluated using TraumaCad (Brainlab). The femoral neck shaft angle and the offset from the tip of the greater trochanter (GT) to the femur's longitudinal axis ('greater trochanter offset') were measured. Five MHS system templates were superimposed on the femur's longitudinal axis, and the distance from the GT tip to MHS's top center was measured. Five independent reviewers each templated 20 images such that all images were measured at least twice. A random sample of five images was selected for all five raters to measure and to calculate an intraclass coefficient Mean IEPs were compared with an independent sample Student t -test. RESULTS: The mean GT offset was 13.5 ± 5.6 mm (range 12.9 to 26.7 mm). The mean neck shaft angle was 129.5 ± 4.0 (range 120 to 139). The mean IEP for nail systems ranged from 5.7 to 7.1 mm medial to the GT tip; there was no notable difference in pairwise comparison of nail systems or in aggregate. Intraclass coefficient for all ratings, measurements, and nail types ranged from moderate to good. Both intra-rater and inter-rater reliability were excellent. DISCUSSION AND CONCLUSION: In a sample with broad variation in femoral anatomy, there is a specific, roughly 1.5 mm wide interval that is 6.4 mm medial to the GT tip that serves as the IEP for the most common MHS systems. No notable difference seems to exist in the IEP among these MHS systems.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".