Relative Bone Width of the Edentulous Maxillary Ridge. Clinical Implications of Digital Assessment in Presurgical Implant Planning
Bibliographic record
Abstract
BACKGROUND: Healthy, well-structured mucosa may clinically disguise atrophic jawbone in preimplant diagnosis. PURPOSE: To analyze bone width in relation to the complete ridge thickness comparing the anterior with the posterior edentulous maxilla. MATERIALS AND METHODS: Data of 52 patients (mean age 62 ± 9 years) who were edentulous for at least 1 year and who received implant treatment were analyzed. Computed tomography (CT) scans were obtained and virtually analyzed in perpendicular sections of 12 maxillary positions (central and lateral incisors, canines, premolars, and first molars) using an implant planning software. Absolute thickness of complete jaw, bone, and mucosa were digitally measured at crestal and basal ridge levels allowing for relative bone width (B-rel) calculation. RESULTS: Mean B-rel at crestal levels was lower than at basal levels (38.6% vs 51.5%, p < .001). Bone width increased significantly (p < .001) in the posterior maxilla at both levels, whereas the thickness of palatal and buccal mucosa was considerably stable. Mean basal B-rel ranged from 49% (6.2 ± 2.0 mm) at incisors to 59% (9.0 ± 2.3 mm) at first molars (p < .001). Mean proportion of regions showing B-rel < 50% were 43% at basal and 80% at crestal levels. CONCLUSIONS: The osseous volume of a large edentulous ridge might be clinically overestimated in preimplant diagnosis, as the relative bone width was generally lower than 50%. Clinicians can use the present results of the virtual bone and mucosa measurements to have a better first estimation of the osseous proportion depending on the maxillary area. However, up to date implant therapy for the edentulous maxilla requires CT-based prosthetically driven implant planning and preferably combination with guided implant placement by transferring planning information to a surgical template.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".