Soft tissue phenotype modification predicts gingival margin long‐term (10‐year) stability: Longitudinal analysis of six randomized clinical trials
Bibliographic record
Abstract
AIM: To assess the prognostic value of soft tissue phenotype modification following root coverage procedures for predicting the long-term (10-year) behaviour of the gingival margin. MATERIALS AND METHODS: Participants from six randomized clinical trials on root coverage procedures at the University of Michigan were re-invited for a longitudinal evaluation. Clinical measurements were obtained by two calibrated examiners. A data-driven approach to model selection with Akaike information criterion (AIC) was carried out via multilevel regression analyses and partial regression plotting for changes in the level of the gingival margin over time and interactions with the early (6-month) results of soft tissue phenotypic modification. RESULTS: One-hundred and fifty-seven treated sites in 83 patients were re-assessed at the long-term recall. AIC-driven model selection and regression analyses demonstrated that 6-month keratinized tissue width (KTW) and gingival thickness (GT) influenced the trajectory of the gingival margin similarly in a concave manner; however, GT was the driving determinant that predicted significantly less relapse in the treatments, with stability of the treated gingival margin obtained beyond values of 1.46 mm. CONCLUSIONS: Among a compliant patient cohort, irrespective of the rendered therapy, the presence of at least 1.5 mm KTW and 1.46 mm GT was correlated with the long-term stability of the gingival margin.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".