Femoral neck radiography: effect of flexion on visualization.
Bibliographic record
Abstract
OBJECTIVES: To determine whether flexion improves radiographic visualization of the femoral neck when the femur is externally rotated. METHODS: Five human femora, with varying neck-shaft and anteversion angles, were measured and immobilized. Degree of flexion required to bring the femoral neck horizontal was measured, varying the rotation. Next, one bone was radiographed in 16 positions, varying rotation in 15 degrees and flexion in 10 degrees increments. Radiographs were presented in randomized blinded fashion to 15 staff radiologists for scoring of femoral neck visualization. Following this, all 5 bones were radiographed in 4 positions of rotation and at 0 degree and 20 degrees flexion, and blinded randomized review of radiographs was repeated. Comparisons between angles and rotations were made using the Mann-Whitney test. RESULTS: The flexion angle required to bring the long axis of the femoral neck horizontal correlated directly with the degree of external rotation (p < 0.05). Visualization of the femoral neck in the extended position progressively deteriorated from 15 degrees internal rotation to 30 degrees external rotation (p < 0.01). However, when 20 degrees flexion was applied to bones in external rotation, visualization significantly improved at 15 degrees (p < 0.05) and 30 degrees (p < 0.01). CONCLUSION: Flexion of the externally rotated femur can bring the femoral neck into horizontal alignment, and a relatively small amount (20 degrees) of flexion can significantly improve radiographic visualization. This manoeuvre could be useful for radiography of the femoral neck when initial radiographs are inadequate because of external rotation of the leg.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".