Gingival Biotype and Its Relationship With the Maxillary Membrane and Lateral Wall Thickness
Bibliographic record
Abstract
The purpose of this study was to analyze the risk of the maxillary sinus lift technique and the correlation between the thickness of the gingiva, maxillary sinus membrane, and the maxillary sinus lateral wall. Cone-beam computerized tomograhy (CBCT) records of 32 adult dentate patients (10 male/22 female) were analyzed. The gingival thickness records of the dental units were compared with the thickness measurements of the membrane and lateral wall of the maxillary sinus. The gingival biotypes varied between 1.1 mm (thin) and 1.6 mm (thick), with a small association with sex. The thickness of the sinus membrane presented a small association between sexes (0.2 mm, female/0.3 mm, male) and gingival biotypes (Cohen d = .52). The lateral wall presented a weak association between the biotypes (1.3 mm, thin/1.1 mm, thick). There was also no correlation between the membrane and lateral wall (r = -.22). The volume dimension related to the graft area of the sinus was 4 mm3 for men and 5 mm3 for women. There was a weak correlation of gingival thickness compared with membrane thickness and lateral wall of the sinus (r = .304/r = -.31). Gingival thickness does not appear to be a reliable thickness predictor of the membrane or lateral wall of the maxillary sinus. The analysis of maxillary sinus anatomical structures through CBCT is the most reliable technique to identify the thickness of the membrane and lateral wall of the maxillary sinus before surgery. We believe that new studies are necessary to confirm our findings.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".