Self-perceived orthodontic treatment need evaluated through 3 scales in a university population
Bibliographic record
Abstract
OBJECTIVE: To evaluate the self-perceived orthodontic treatment need in a university population evaluated through 3 scales that used different approaches. DESIGN: Cross-sectional survey. SETTING: University dental clinic, Lima, Peru, 2001. MATERIALS AND METHODS: Questionnaires that gathered perceptions on dentofacial aesthetic perception and orthodontic treatment need were applied to a randomly selected sample (329) of first year university students (729). Subjects undergoing orthodontic treatment at the time of examination were excluded. MAIN OUTCOME MEASURES: Aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN), Oral Aesthetics Subjective Index Scale (OASIS) and a visual analogue scale (VAS) were used. STATISTICAL ANALYSIS: Descriptive statistics, Spearman correlation test, Kruskall-Wallis test and Mann-Whitney U-test were used. RESULTS: For the AC, 87.5% were in the "without treatment need" category, 10.6% in the "borderline need" category and 1.8% in the "treatment need" category. The mean AC score was 3.02 (+/-1.49). The mean OASIS score was 11.81 (+/-4.84), and the VAS score was 40.16 (+/-18.16). Correlations between the 3 self-assessment scales were moderate (AC-OASIS 0.416, AC-VAS 0.541 and OASIS-VAS 0.457). Gender or previous orthodontic treatment had no influence (p<0.05) on the scales. CONCLUSIONS: Differences in the approaches used by each scale to evaluate the self-perception of the aesthetical arrangement of the front teeth may explain the moderate correlation values.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".