Anthropometric study of facial indices among Bangladeshi women
Bibliographic record
Abstract
Background: Craniofacial anthropometric values and indices are vital for experts from different walks of science. Variations in such values are evident in racial and geographical attribute. Furthermore, anthropological classification would assist clinicians in planning regional surgeries, forensic identification and many more. Objective: In this study we aimed to measure the head-face landmarks in a particular population and to correlate their association. Methodology: To assess intra population variation, the fronto-occipital circumference, facial height, bizygomatic breadth, bitragion breadth, bigonial breadth, width of mouth, intercanthal width, biocular breadth and body height of 100 Bangladeshi women (age 25-45 years) were measured and craniofacial indices were calculated. Frequencies were observed while comparing the variables by ANOVA using SPSS version 17. Result: The mean values of facial indices revealed as prosopic index 103.8 ± 12cm, zygomandibular index 81.1 ± 7.44cm, canthal index 36.93 ± 2.3cm and circumference-interorbital index 2.26 ± 0.4cm. 86% of subjects were clustered to hyperleptoprosope group (very narrow face) and 69% had wide jaw with closely placed eyes. No significant (p>0.05) correlation was denoted between variables and facial indices. Conclusion: The result of this study would provide an access to baseline data of local standards for anthropometric evaluation which might help the clinicians in planning regional surgeries and forensic experts in identification. Northern International Medical College Journal Vol. 12 No.1 July 2020, Page 503-506
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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 | 0.000 | 0.001 |
| 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".