Accuracy of interproximal enamel reduction during clear aligner treatment
Bibliographic record
Abstract
AIM: The aim of the present study was to compare the accuracy of the actual space obtained through interproximal enamel reduction (IPR) compared to the amount of IPR planned through the digital setup during clear aligner treatment (CAT). MATERIALS AND METHODS: A total of 10 clinicians were randomly recruited using the Doctor Locator by Align Technology (California). For each clinician, four consecutive patients treated with CAT and manual stripping were selected for a total of 40 subjects and 80 dental arches. For each patient, the amount of planned IPR and the amount of actual IPR performed were recorded. Each arch was considered individually. For each arch, the mesio-distal tooth measurements were obtained from second to second premolars. RESULTS: No systematic measurement errors were identified. In 25 cases, stripping was planned and performed in both arches; in 4 cases only in the upper arch and in the remaining 7 cases only in the lower arch. The difference between planned IPR and performed IPR was on average 0.55 mm (SD, 0.67; P = 0.022) in the upper arch and 0.82 mm (SD, 0.84; P = 0.026) in the lower arch. The accuracy of IPR in the upper arch was estimated to be 44.95% for the upper arch and 37.02% for the lower arch. CONCLUSION: Overall, this study showed that the amount of enamel removed in vivo did not correspond with the amount of IPR planned. In most cases, the performed IPR amount was lower than planned. When considering the actual amount in millimeter, these differences may not be considered clinically relevant.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".