Mandibular residual ridge morphology in relation to complete dentures and implant overdentures–Part I: Predictors for perceived conventional denture stability
Bibliographic record
Abstract
BACKGROUND: There is lack of reliable predictors for success of conventional complete denture (CCD) therapy, which in turn might affect the effectiveness of subsequent implant-retained overdenture (IOD) therapy. PURPOSE: To investigate relationships between digitally obtained geometrical mandibular residual ridge measures and perceived CCD-stability. MATERIALS AND METHODS: 30 CCD wearing patients (67.9 ± 7.0 years) for whom a new set of CCDs was advised, were treated with new CCDs. Digitalized mandibular gypsum models were measured using the Geomagic Studio 2013 software. Data were obtained for (1) height, width, and cross-section surface area of the residual ridge at different locations (midline, premolar, and anterior edge of retromolar pad) and (2) denture base surface area. Scatter plots and multivariate regression analyses were used to investigate associations between the geometric data and denture base surface area, and correlated with denture stability scores (Spearman rank test). RESULTS: = 0.796). Ridge morphology variables, except width at midline location, were significantly correlated with CCD-stability (p-values <0.05). CCD-stability was significantly correlated with denture base surface area (p ≤ 0.001). CONCLUSION: Residual ridge height at premolar location was most predictive for denture base surface area and perceived CCD-stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".