The predictability of transverse changes with Invisalign
Bibliographic record
Abstract
OBJECTIVES: To investigate the predictability of arch expansion using Invisalign. MATERIALS AND METHODS: Sixty-four adult white patients were selected to be part of this retrospective study. Pre- and posttreatment digital models created from an iTero scan were obtained from a single orthodontist practitioner. Digital models from Clincheck were also obtained from Align Technology. Linear values of upper and lower arch widths were measured for canines, premolars, and first molars at two different points: lingual gingival margins and cusp tips. A paired t-test was used to compare expansion planned on Clincheck with the posttreatment measurements. Variance ratio tests were used to determine if a larger change planned was associated with larger error. RESULTS: For every maxillary measurement, there was a statistically significant difference between Clincheck and final outcome (P < .05), with prediction worsening toward the posterior region of the arch. For the lower arch measurements at the gingival margin, there was a statistically significant difference between the Clincheck planned expansion and the final outcome (P < .05). Points measured at the cusp tips of the lower arch teeth showed nonstatistically significant differences between Clincheck prediction and the final outcome (P > .05). Variance ratios for upper and lower arches were significant (P < .05). CONCLUSIONS: The mean accuracy of expansion planned with Invisalign for the maxilla was 72.8%. The lower arch presented an overall accuracy of 87.7%. Clincheck overestimates expansion by body movement; more tipping is observed. Overcorrection of expansion in the posterior region of the maxillary arch seems appropriate.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".