Effect of Drill Handle Force Applied to Digital Surgical Guides on Implant Deviation: An In Vitro Study
Bibliographic record
Abstract
INTRODUCTION: This in vitro study evaluated how different forces applied to the dental drill handle during static computer-assisted implant surgery influence surgical guide deformation and implant placement accuracy. METHODS: Twenty-four virtual implants were divided into six groups (0-10 N, in 2 N increments). Surgical guides were scanned under loaded conditions, and deviations were quantified by superimposition with the baseline model. RESULTS: At high forces (≥ 8 N), buccal and palatal deformations increased markedly, with the 10 N group showing the largest displacement and angular deviation. Apical deviations (up to 1.554 mm) exceeded platform deviations (0.720 mm), and angular changes reached 9.595°. Under low forces (2-4 N), deformations were minimal, and anterior regions showed greater stability than posterior regions. CONCLUSION: Forces above 6 N approach or exceed clinically acceptable thresholds, while 0 to 4 N produced the most precise outcomes. A single overload (≥ 8 N) can jeopardize surgical precision and long-term implant stability, underscoring the importance of controlling drill handle force. Clinicians are therefore advised to limit applied forces to ≤ 4 N to preserve surgical accuracy and minimize guide distortion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".