A Comparison of Short-Term Treatment Outcomes with Non-extraction and Extraction Orthodontic Treatment Modalities in Borderline Class I and Mild Class II Malocclusions
Bibliographic record
Abstract
Extraction (EX) and non-extraction (NEX) decisions impact various treatment outcomes, including soft tissue and incisor positions. Increasing desirability for more protrusive lips requires for an update in esthetic preferences. Borderline cases are critical when attempting to discern EX and NEX treatment effects. This retrospective study used Discriminant Analysis to identify 30 EX and 30 NEX borderline cases, analyzing soft tissue, incisor, and occlusal changes. Assessment of profile and incisor inclination preferences were conducted by 90 laypeople (LP), 40 orthodontists (OR), and 40 general dentists (GP). EX cases showed increased nasolabial angle, more ideal overbite, and lip and incisor retraction, with no significant differences in molar relationship and overjet. Linear regression of survey results indicated OR preference for EX profiles, GP preference for NEX profiles, and all groups favouring more upright incisors. LP’s preferences more aligned with GP. Conflicting GP preferences emerged, desiring protrusive lips and acute nasolabial angle (NEX-associated), yet more strongly preferring upright incisors (EX-associated).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".