Age and sex-related variations in facial soft tissue thickness in a sample of Pakistani children
Bibliographic record
Abstract
A facial soft tissue thickness (FST) database forms the backbone of different facial reconstruction methods. Various studies have identified age, sex and ethnicity as the core factors affecting the FST of an individual. The aims of this study were to explore the changes in FST occurring during the adolescent growth period and to develop the FST database for Pakistani children. The lateral cephalograms of 231 children, aged 9–18 years, were analysed and FST was determined at the 11 midline points. Subjects were divided into five age groups (9–10, 11–12, 13–14, 15–16 and 17–18 years) to evaluate age-related variations in FST. To compare FST between males and females and among different age groups, multivariate analysis of variance (MANOVA) was used. Moreover, the FST of Pakistani children was compared with those of Japanese and Canadian-Caucasoid children. Significant age-related variations in FST were present at four landmarks in boys and at six landmarks in girls. Marked ethnic differences in FST (>2 mm) were also observed at five landmarks in some of the age groups. These age-related and ethnic variations in FST warrant the use of data of appropriate age groups for a specific population for reliable outcome of facial reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".