Initial forces experienced by the anterior and posterior teeth during dental-anchored or skeletal-anchored en masse retraction in vitro
Bibliographic record
Abstract
OBJECTIVE: To investigate initial forces acting on teeth around the arch during en masse retraction using an in vitro Orthodontic SIMulator (OSIM). MATERIALS AND METHODS: The OSIM was used to represent the full maxillary arch in a case wherein both first premolars had been extracted. Dental and skeletal anchorage to a posted archwire and skeletal anchorage to a 10-mm power arm were all simulated. A 0.019 × 0.025-inch stainless steel archwire was used in all cases, and 15-mm light nickel-titanium springs were activated to approximately 150 g on both sides of the arch. A sample size of n = 40 springs were tested for each of the three groups. Multivariate analysis of variance (α = 0.05) was used to determine differences between treatment groups. RESULTS: In the anterior segment, it was found that skeletal anchorage with power arms generated the largest retraction force (P < .001). The largest vertical forces on the unit were generated using skeletal anchorage, followed by skeletal anchorage with power arms, and finally dental anchorage. Power arms were found to generate larger intrusive forces on the lateral incisors and extrusive forces on the canines than on other groups. For the posterior anchorage unit, dental anchorage generated the largest protraction and palatal forces. Negligible forces were measured for both skeletal anchorage groups. Vertical forces on the posterior unit were minimal in all cases (<0.1 N). CONCLUSIONS: All retraction methods produced sufficient forces to retract the anterior teeth during en masse retraction. Skeletal anchorage reduced forces on the posterior teeth but introduced greater vertical forces on the anterior teeth.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".