Effects of Pilot Hole Size and Bone Density on Miniscrew Implants' Stability
Bibliographic record
Abstract
PROBLEM: How does pilot hole size and bone density affect the primary stability of miniscrew implants (MSIs)? METHODS: Using 120 MSIs divided equally into six groups, this 2 × 3 factorial design evaluated the effects of synthetic bone density (0.64 g/cc vs 0.8 g/cc cortices) and pilot hole size (no pilot hole, 1.0 mm pilot hole, and 1.4 mm pilot hole) on maximum insertion torque and pullout strength. The maximum placement torque was measured as the last thread of the MSIs was inserted. The pullout strength test applied a vertical force at 10 mm/min until failure. RESULTS: The insertion torque and pullout strength values were significantly (p ≤ .05) greater for the MSIs placed in high-density than in low-density cortical bone. The insertion torque and pullout strength decreased as pilot hole size increased, with significant (p ≤ .05) differences between all three subgroups. Insertion torque and pullout strength were significantly intercorrelated for all subgroupings, with stronger correlations in denser bone having smaller or no pilot holes. CONCLUSION: Depending on bone density, pilot holes of limited size can be used to optimize primary stability by decreasing insertion torque while maintaining the pullout strength of bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".