Sex Estimation in Terms of Inclination and Alsberg in Proximal Femur by using Machine Learning Algorithms
Bibliographic record
Abstract
The fact that the femur has a solid structure ensures that the integrity of the bone is preserved, making it favorable in the sex determination process. In this study, it was aimed to predict sex by using angular variables of femur from computed tomography (CT) images and machine learning algorithms. In this study, a total of 4 angular measurements, including the femoral inclination angle (FIA) and Alsberg angle (FAA) of the proximal femur on both sides, were evaluated on CT images of 88 female and 92 male adults. Logistic regression (LR) and classification and regression tree (CART) machine learning algorithms were used for sex diagnosis. 5-fold cross-validation method was used in the training and testing processes of the models. Model performances were evaluated with area under the ROC curve, Precision and Recall statistics. Of the 4 angle measurements evaluated, only the right side FIA mean was significantly higher in women (p=0.042). The sex diagnosis success of the LR model and the CART algorithm were found to be similar (p values 0.014 and 0.017, respectively). When the success criteria of each algorithm were examined, we saw that although sex estimation was significant, (Acc 0.61, Acc 0.60, respectively) they were not very successful. We found that the machine learning algorithms applied to the variables of proximal femur angle parameters gave low accuracy of sex and the effect of both models on sex estimation was similar.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".