Measurement properties of gingival biotype evaluation methods
Bibliographic record
Abstract
BACKGROUND: There are numerous methods to measure the dimensions of the gingival tissue, but few have compared the effectiveness of one method over another. OBJECTIVE: This study aimed to describe a new method and to estimate the validity of gingival biotype assessment with the aid of computed tomography scanning (CTS). MATERIALS AND METHODS: In each patient different methods of evaluation of the gingival thickness were used: transparency of periodontal probe, transgingival, photography, and a new method of CTS). Intrarater and interrater reliability considering the categorical classification of the gingival biotype were estimated with Cohen's kappa coefficient, intraclass correlation coefficient (ICC), and ANOVA (P < .05). The criterion validity of the CTS was determined using the transgingival method as the reference standard. Sensitivity and specificity values were computed along with theirs 95% CI. RESULTS: Twelve patients were subjected to assessment of their gingival thickness. The highest agreement was found between transgingival and CTS (86.1%). The comparison between the categorical classifications of CTS and the transgingival method (reference standard) showed high specificity (94.92%) and low sensitivity (53.85%) for definition of a thin biotype. CONCLUSION: The new method of CTS assessment to classify gingival tissue thickness can be considered reliable and clinically useful to diagnose thick biotype.
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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".