Effect of a Bone‐Flattening Drill on the Accuracy of Fully Guided Implant Surgery: An In Vitro Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether a bone-flattening drill improves implant placement accuracy in static, computer-assisted implant surgery across different ridge morphologies. MATERIALS AND METHODS: Thirty 3-D printed maxillary models, each featuring a flat and a 20° sloped healed ridge, were randomly allocated to three drilling protocol groups (n = 10 models per group, 60 total implants). In the flattening-drill group (FL), bone preparation was performed with a flattening drill followed by the manufacturer's drilling sequence. The initial drill group (IN) began directly with the initial drill and subsequent drilling sequence, while the final drill group (FN) commenced from the final drill. Fully guided surgical templates with resin sleeves were used. Positional accuracy-platform, apex, angular, and depth deviation-was assessed by digital superimposition of planned versus actual implant positions. Statistical analyses were performed using two-way analysis of variance with post hoc comparisons. RESULTS: The FL group demonstrated significantly superior accuracy over the conventional drilling protocols across all parameters. The platform deviation in the FL group (0.36 ± 0.17 mm) was lower than the IN group (0.57 ± 0.21 mm) and FN group (0.99 ± 0.43 mm) (p < 0.001). The angular deviation showed a similar pattern, being 2.92° ± 1.13°, 4.17° ± 1.48°, and 5.95° ± 2.84° in the FL, IN, and FN groups, respectively (p < 0.001). The ridge inclination significantly affected accuracy in the FL and IN groups, while the FN group showed consistently poor accuracy regardless of the ridge morphology. The 95% confidence intervals in the FL group remained within clinically acceptable ranges for both flat and sloped healed ridges. CONCLUSIONS: The use of a bone-flattening drill as an initial drilling step significantly improved the guided implant surgery accuracy compared with conventional protocols. This modification offers a practical solution for achieving predictable implant positioning, especially on sloped ridges, thereby supporting optimal prosthetic outcomes. CLINICAL SIGNIFICANCE: The bone-flattening-drill protocol might provide clinicians with a reliable method for increasing implant placement accuracy to within clinically acceptable ranges and reducing the risks of prosthetic complications and revision procedures. Further in vivo validation is required.
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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 | 0.003 |
| 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 | 0.000 |
| 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".