The impact of orthodontic treatment on normative need. A case-control study in Peru
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of previously provided orthodontic treatment on the normative need in a sample of young adult Peruvians. METHODS: Six hundred and thirty five freshmen, representative of all first year students registering in 2002 at a private university in Lima, were randomly screened to obtain 63 cases and 126 controls. A case was defined as having a definite orthodontic treatment need determined by the DAI and IOTN indices simultaneously. A control was defined as having no need of orthodontic treatment based on both indices. Students were also asked if they had previously undergone any orthodontic treatment. Binary logistic regression was used for the statistical analysis. RESULTS: Sex, age and socioeconomic status of the students were not statistically associated with normative orthodontic treatment need (p = 0.258, 0.556 and > or = 0.272 respectively). The percentage of students with a previous history of orthodontic treatment was similar between the cases and the controls (14.3 per cent and 11 .9 per cent respectively). There were no statistically significant associations between the variables. CONCLUSIONS: The impact of previously provided orthodontic treatment on the current normative need of young adults was limited. Properly designed studies are required to assess the reasons for these findings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".