Bibliographic record
Abstract
Introduction: The aims of the cross-sectional study were to determine the prevalence of malocclusion and orthodontic treatment need according to the Index of Complexity, Outcome, and Need (ICON) among schoolchildren of the Aboriginal Island Community of Haida Gwaii in Northwestern British Columbia, Canada. Methods: Out of 535 schoolchildren, 215 (90 boys and 125 girls) agreed to participate in the clinical examination. An examiner, trained in occlusal indices, assessed orthodontic treatment need in children employing the ICON score and the ICON complexity grade. In addition, a questionnaire was modified and used to measure the schoolchildren’s expectations of and self- perceived need for orthodontic treatment. Out of the 215 schoolchildren that were clinically examined, 192 were old enough to comprehend the questions in the questionnaire and were capable of completing the questionnaire without any assistance. Results: The mean age (N=215) was 12.9±2.8 years. Of the examined schoolchildren, 67% have Aboriginal ancestry (at least one parent is Aboriginal). The mean ICON score was 43.5±26.2. There were no statistically significant differences in ICON scores regarding gender (t-test, P=0.207), ethnicity (t-test, paternal ethnicity P=0.886 and maternal ethnicity, P=0.389), or school (ANOVA with Post Hoc Bonferroni Adjustment P=0.317). Overall, 43.7% of the schoolchildren needed orthodontic treatment (ICON > 43). Based on the ICON complexity, 31% of the schoolchildren had moderate to very difficult malocclusions to treat. Adolescents (N=192) with higher ICON complexity scores (more complicated malocclusions) felt a greater need for orthodontic treatment, for extractions, that treatment would give them confidence, and that they were willing to be treated either on island or off island (One way ANOVA with Post Hoc Bonferroni Adjustment, P<0.001, 0.014, 0.002, <0.001, 0.007 respectively) compared to those with lower ICON scores. Conclusions: There is need for orthodontic treatment among Haida Gwaii schoolchildren (43.7% of adolescents need orthodontic treatment according to their ICON scores). As 31% had moderate to very difficult malocclusions to treat, specialty orthodontic services are recommended. The adolescents’ self-perceived need for orthodontic treatment corresponded to their clinical need; i.e. schoolchildren with higher ICON complexity scores felt a greater need for orthodontic treatment than those with lower ICON complexity scores.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".