Effects of Three‐Dimensional Print Offsets of Sleeves on Implant Placement Trueness: An In Vitro Study
Bibliographic record
Abstract
INTRODUCTION: This study assesses the effects of different three-dimensional (3D) print offsets of sleeves on sleeve and implant placement trueness in guided implant surgery. METHODS: Twenty-five sleeves were installed on 3D-printed surgical guides, with sleeve offsets of 0.02, 0.04, 0.06, 0.08, and 0.10 mm (five groups with five sleeves per group). The actual surgical guides were scanned to generate digital files, which were compared with the reference group based on design positions. The experimental verification and evaluation method involves assessing sleeve installations and measuring the deviations between the actual position of the sleeve and implant compared with the reference group. RESULTS: The sleeve installations in the 0.02-mm offset group were not acceptable, as there were visible cracks in the sleeve; in the case of the 0.04-mm offset group, there were also installation difficulties. At the sleeve position level, there were larger deviations in the 0.10-mm offset group, whereas the 0.08-mm group had smaller deviations in the labial and palatal directions (p < 0.05). At the implant level, the 0.10-mm offset group showed a significantly larger deviation (labial: 0.453 ± 0.165 mm; palatal: 0.399 ± 0.160 mm; p < 0.05). As for the mesial-distal direction, there were no significant differences between the groups. CONCLUSIONS: Print offsets of sleeves had a significant effect on the ease of installation and the placement accuracy of sleeves and implants. Under the printer and sleeves conditions used in this experiment, smaller offsets (0.02 and 0.04 mm) caused installation difficulties and misalignments. Larger offsets (0.10 mm) created noticeable deviations. Sleeve offsets of 0.06 and 0.08 mm provided a balance between ease of installation and high placement trueness, making them more suitable in this experiment. Excessive or insufficient print offsets of sleeves can affect installation conditions and positional trueness of sleeves and implants.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".