Georeferencing of adolescents with malocclusion in a capital of Southern Brazil
Bibliographic record
Abstract
The aim of this study was to analyze the prevalence and to georeference the malocclusion traits in adolescents in the city of Curitiba, Paraná, Brazil. Data from a previous cross-sectional study with 538 adolescents aged 10 to 14 years were used. In addition, the following variables were used: gender, Health District (HD) of residence, and presence and malocclusion traits. Fisher’s Exact Test, georeferencing, and kernel mapping were used for data evaluation. Malocclusion was observed in 52.4% of individuals, and the most prevalent occlusal trait was deep bite (22.7%), followed by excessive overjet (19.9%), anterior crowding (8.0%), posterior crossbite (6.5%), anterior open bite (4.8%), and anterior crossbite (1.7%). Malocclusion was not associated with gender (p = 0.389) or HD (p = 0.079). However, when stratified by gender, the deep bite prevailed among male. The highest malocclusion trait’s prevalence was observed in the HDs of Cajuru, Pinheirinho, Boa Vista, and Cidade Industrial de Curitiba. Despite the absence of significant differences in relation to gender and HD, the prevalence of malocclusion traits in the sample studied was high, especially for deep bite. Additionally, georeferencing proved to be useful for identifying the distribution of malocclusion in Curitiba.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".