Gingival phenotype determination: Cutoff values, relationship between gingival and alveolar crest bone thickness at different landmarks
Bibliographic record
Abstract
Background/purpose: Gingival phenotype (GP) has been reported to influence the treatment planning and clinical outcomes in several dental specialties. This study aimed to investigate optimal cutoff values for gingival thickness (GT) measurement at different landmarks to determine GP. The correlations between GT and bone thickness (BT) of buccal alveolar crest were also analyzed. Materials and methods: A total of 600 teeth were included. GP was clinically determined by the transparency of a periodontal probe through the gingival margin (TRAN). Measurements for free gingival thickness (GT1), cementoenamel junction gingival thickness (GT2), supracrestal gingival thickness (GT3), subcrestal 1 mm gingival thickness (GT4) and BT at 1, 3 mm apical from the alveolar crest edge (BT1 and BT2) were assessed on cone-beam computed tomography (CBCT) images. Spearman's correlation coefficient was used to evaluate correlations between GT and BT. Results: The optimal cutoff values of GT using CBCT method to discriminate GP were 0.75 mm for GT1, 0.85 mm for GT2, 1.15 mm for GT3 and 0.45 mm for GT4. There was significantly positive correlation between GT and BT at all levels (r: 0.375-0.903). The correlations between GT3 and BT (r: 0.789-0.903) were strong, while correlations between GT4 and BT were weak (r: 0.375-0.467). Conclusion: The optimal cutoff values of gingival thickness using CBCT method to discriminate gingival phenotype at each gingival landmark were determined. The supracrestal gingival thickness might be an indicator of buccal alveolar crest bone thickness, which could provide valuable perspectives on clinical diagnosis, treatment planning and decision-making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".