VALIDATION OF THE LESSER TROCHANTER PROFILE FOR PREDICTION OF FEMORAL ROTATION
Bibliographic record
Abstract
Purpose Incidence of malrotation of femoral fractures after intramedullary nailing is as high as 28%. Prevention of malrotation is superior to late derotation osteotomy. The lesser trochanter (LT) profile has been in use for some time as a radiographic landmark of femoral rotation. One of the authors has previously described a linear regression model that describes the relationship of the LT to rotation. This paper aims to validate the use of this equation in predicting femoral rotation. Method A survey was created and circulated online. Twenty images of cadaveric femurs of known rotation were chosen randomly from a large series. Thirty individuals with varying degrees of orthopaedic experience were invited to participate. Participants were asked to take measurements of the LT in a standardized fashion. Inter-observer variation for predicted rotation and the precision of predicted rotation was calculated. Results were grouped into those with the LT readily visible and those with the LT hidden by the femoral shaft. Results A pilot study found the standard deviation for films with the LT hidden was 10.8 degrees, and only 6.0 degrees for films with the LT visible. The mean difference between the predicted and actual rotation was equally high in both groups (18.3 and 17.3 degrees respectively). Conclusion Preliminary results found that the LT must be clearly visible to predict femoral rotation. This suggests that the surgeon should place the femur in a neutral or externally rotated position. In a favourable position most predictions were within a 6.0 degree spread, which would be sufficient to prevent a fifteen degree malrotation. Predicted rotation was however not precise enough to prevent a fifteen degree malrotation, regardless of LT visibility. The precision of predicted rotation may be improved by using a non-linear model. Such a model has recently been designed by a group of engineers at the University of Manitoba. The r squared value of the non-linear model was 0.88, in comparison to 0.78 for the linear equation. Precision may be further improved by using the contra-lateral LT for comparison.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".