Age and sex estimation by cranial and paranasal sinus metric measurements of Turkish citizens using PLS methods
Bibliographic record
Abstract
‘Identification’, where age and sex estimation are two main components, is one of the important issues of forensic medicine. In particular, sex estimation from skeletal remains and age estimation of unidentified people are frequently encountered. This study aimed to estimate age and sex with radiological imaging of the skull by evaluating cranial CT (computed tomography) images, cranial metric measurements frequently used in the literature, and paranasal sinus measurements. Using cranial CT images of 803 cases, 404 of whom were male (50.3%) and 399 of whom were female (49.7%), between the ages of 10–65, analyzed with PLS (partial least squares) regression model for age estimation, different models were created for sex estimation and statistical results were discussed with the literature. While only cranial metric measurements were able to correctly predict sex at a rate of 77% and only paranasal metric measurements at a rate of 70%, this rate increased to 79% when all measurements were used together. For age estimation, formulas were developed with the four parameters that we found to have the most statistically significant discriminatory power (nasal width, nasal height, right maxillary sinus width and left maxillary sinus depth).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.030 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".