A method of estimating age of undocumented children and young adults of different socioeconomic status in Cambodia
Bibliographic record
Abstract
A growing number of Cambodian children without legal documentary evidence of date of birth are vulnerable to exploitation. This study aimed to evaluate the applicability of southern Chinese reference dataset for dental age estimation on Cambodian children and young adults of different socioeconomic status. Dental panoramic tomographs (DPT) of 371 Cambodian children and young adults belonging to lower and higher socioeconomic status (SES) groups were analyzed. All the left maxillary and mandibular permanent teeth including the third molars were scored based on Demirjian's classification of tooth development stages. Chronological age (CA) was calculated from the date of birth and date of exposure of radiograph. The mean age of attainment for each stage of development was obtained from the southern Chinese reference dataset. Dental age (DA) was calculated by averaging the mean age scores for all the teeth. Paired t-test and correlation analysis were conducted to measure associations between the chronological age and the dental age for males and females in the lower and higher socio-economic status groups. Underestimation of age was observed in both SES groups using the southern Chinese reference dataset. For the higher SES group, the difference between the chronological and dental age (CA-DA) was 0.26 years for females and 0.11 years for males. The difference was statistically significant only in females (p < 0.05). In the low SES group, the results showed a difference of 0.07 years in females and 0.01 years in males; the differences were not statistically significant in both sexes (p > 0.05). A strong correlation was observed between the CA and DA in both sex and SES groups ranging from 0.969 to 0.988 (p < 0.05). The southern Chinese dental reference dataset can be used to estimate the age of undocumented Cambodian male and female children and young adults of both higher and lower SES.
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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 | 0.000 | 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 | 0.000 | 0.003 |
| 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".