A Comparative Assessment of Hard Palate Thickness using Micro‐CT and Gross Cadaveric Measurements: Implications for the Safe Placement of Orthodontic Miniscrews
Bibliographic record
Abstract
The hard palate is a preferred area for orthodontic miniscrew (OMS) insertion due to easy surgical access and favorable anatomical configuration. However, accurate measurement of palatal bone thickness (BT) is crucial for choosing appropriate OMS lengths and insertion sites. The aim of this study is to determine the accuracy of micro‐computed tomography (micro‐CT) for assessing palatal BT, and to establish which sites commonly provide adequate BT for safe OMS placement. Ten cadaveric hard palates (52‐98 years) were cleaned of soft tissue and imaged using micro‐CT. Bone thickness was measured from sagittal micro‐CT image slices at pre‐determined sites beginning 3mm posterior to the incisive foramen and 4mm lateral to the midpalatal suture. Micro‐CT analysis was carried out using MicroView Image Viewer and Analysis tool. The same sites on each palate will also be measured from sagittal sections using digital caliper. Initial micro‐CT results indicate that palatal bone thickness is heterogeneous. The palate thins from anterior to posterior and also medio‐laterally when comparing sites 4 mm vs. 8mm adjacent to the midline. It is expected that similar results will also be observed from caliper measurements of palatal bone thickness. Early findings suggest that BT is favorable for OMS insertion in the anterior palate and that the posterior palate should be avoided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".