Prediction of mesiodistal canine and premolar tooth width in a sample of Peruvian adolescents
Bibliographic record
Abstract
OBJECTIVES: To compare the predicted tooth width measurements of permanent canine and premolars from Tanaka-Johnston regression equations and Moyers probability tables with the in situ measurements in a sample of Peruvian adolescents. DESIGN: Cross-sectional. SETTING AND SAMPLE POPULATION: Trujillo, Peru; 248 dental casts were measured using a sliding caliper with a Vernier scale rounded to 0.1 mm. RESULTS: Tanaka-Johnston regression equations were not precise, except for the upper arch in the male sample. For females, the Moyers 95th percentile in the upper arch and the 65th percentile in the lower arch predicted the sum precisely. For males, the Moyers 65th percentile for the lower arch predicted the sum precisely, but none of the Moyers percentiles provided precise prediction in the upper arch. CONCLUSIONS: Using tooth width prediction methods from a different racial origin could create an under- or overestimation of the actual combined canine and premolar tooth width, although their clinical significance is disputable.
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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 | 0.001 | 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 | 0.008 |
| 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".