Accuracy of immediately placed implants using surgical guides from different <scp>3‐dimensional</scp> printers: <scp>An</scp> in vitro study
Bibliographic record
Abstract
AIM: The aim of this study was to evaluate the accuracy of 3-dimensional (3D)-printed surgical guides for fully guided immediate implants from different manufacturers. METHODS: Eighteen 3D printed fully guided surgical guides (split into 3 groups [n = 6] according to their manufacturer: Company, Desktop, or Lab), were used to place 72 implants (n = 24) in identical maxillary models. After placement, the mean global, angular, mesiodistal, buccopalatal, and vertical deviation at the platform and apex of the placed implants, relative to their preoperatively planned positions, was calculated. RESULTS: Significant differences in global apex deviation, angular deviation, mesiodistal apex deviation, and vertical platform and apex deviation were found between the Lab and Desktop groups (p ≤ 0.007). Significant differences in mesiodistal platform and apex deviation and buccopalatal apex deviation were also found between the Company and Desktop groups (p ≤ 0.005). Finally, significant differences in buccopalatal apex deviation, and vertical platform and apex deviation were found between the Company and Lab groups (p ≤ 0.003). Mean differences between guide groups across all parameters never exceeded 0.5 mm or 1°. CONCLUSIONS: The choice of 3D printer has a significant effect on the accuracy of fully guided immediate implants. However, the clinical relevance of these differences may be considered limited.
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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".