Human tooth movement by continuous high and low stresses
Bibliographic record
Abstract
OBJECTIVE: To compare three-dimensional tooth movements resulting from relatively higher and lower stresses in a split-mouth design. MATERIALS AND METHODS: Eight volunteers whose maxillary first premolars were removed for orthodontic treatment participated. Each subject's maxillary canines were retracted by randomly assigned constant stresses of 78 kPa and 4 kPa via segmental mechanics. Dental casts depicting 8-10 visits per subject over 84 days and a three-axis microscope were used to measure movements serially. Descriptive statistics and mixed linear modeling were applied for data analyses (α = .05). RESULTS: Teeth moved by 78 kPa had significantly faster (P = .0005) distal movement (0.066 ± 0.020 mm/day) compared to teeth moved by 4 kPa (0.031 ± 0.012 mm/day). Lateral movement and distopalatal rotation were also significantly faster (fourfold and 10-fold, respectively) with higher than with lower stress (P < .0001). Average extrusion-intrusion, crown torque, and tip were small (≤ |0.25| mm, |2.29|°, and |1.98|°, respectively), fluctuated, and not significantly different between high and low stresses. No lag phase of tooth movement was evident. CONCLUSIONS: Maxillary canines were retracted faster by 78 kPa than by 4 kPa. Controlled translation was possible with 4 kPa, but 78 kPa outstripped appliance constraints, causing distopalatal rotation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".