Suggested mesiodistal distance for multiple implant placement based on the natural tooth crown dimension with digital design
Bibliographic record
Abstract
PURPOSE: The purpose of this investigation was to identify a mesiodistal algorithm for multiple posterior implant placement based upon an ideal prosthetically restoration design. METHODS: One hundred one cases of posterior free-end edentulous arches were selected for digital crown designs and measurements. Cone bean computed tomogram and digital fabricated crown were applied. DICOM files were exported to a viewer software (BlueSkyPlan4) to generate digital crown and measurement. The mesiodistal space between roots of adjacent teeth and center of the potential implant horizontally, from both cross-section and coronal plane were measured. Comparisons were performed using t-tests. RESULTS: No significant difference was found in the distances of the maxillary and mandibular posterior implants to adjacent natural teeth (p > 0.05). For interdental/implant distances, premolars are around 4.2 mm and molars are 5.4 mm, correspondently. The second premolar interimplant distance is around 7-7.4 mm. The distance of interimplant of the first molar is about 8-8.5 mm. For the maxillary second molar, the interimplant distance is 9.26 ± 0.29 mm and the mandibular second molar interimplant distance is 9.58 ± 0.19 mm, which is significantly different. No difference was found between the two different measurement methods. CONCLUSION: A mesiodistal algorithm of 4-4.6 (implant to adjacent canine tooth), 7-7.4, 8-8.5, and 9-9.5 mm was recommended for interimplant/tooth distance from first premolar to second molar when placing implants with or without case-specific prosthetic planning prior to surgery.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".