Retrospective clinical audit of extraction cases treated with clear aligner therapy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of clear aligners in premolar extraction cases by measuring the differences between predicted and achieved tooth movements. MATERIALS AND METHODS: The sample consisted of 32 patients undergoing extraction treatment with clear aligners, with a mean age of 21.2 (+-7.6) years. Discrepancies between achieved and predicted tooth movements were determined using paired t-tests and independent t-tests. The discrepancies per tooth group were assessed per dental arch and were evaluated for clinical significance (> 2 degrees; >0.5 mm). RESULTS: Linear discrepancies that demonstrated clinical significance (> 0.5 mm) in the maxillary arch were the buccal-lingual and occlusal-gingival discrepancies for the central incisors (0.61 mm and 0.87 mm) and the buccal-lingual discrepancy of the first premolars (0.59 mm). The first and second molars in the mandibular arch showed buccal-lingual discrepancies of 0.51 mm and 0.65 mm, respectively. In comparison, the central (0.66 mm) and lateral incisors (0.57 mm) and first and second premolars (both 0.55 mm) showed clinically significant discrepancies in the occlusal-gingival direction. All angular discrepancies in the maxillary and mandibular dentition were statistically and clinically significant (> 2 degrees). CONCLUSIONS: Loss of torque and occlusal-gingival discrepancies of the upper and lower incisors were clinically significant. Unplanned tipping of teeth adjacent to the extraction site was also clinically significant. These factors should be considered and mitigated at the ClinCheck stage to aid efficiency.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".