A double blind randomized clinical trial comparing lingualized and fully bilateral balanced posterior occlusion for conventional complete dentures
Bibliographic record
Abstract
PURPOSE: A lingualized occlusion (LO) for complete dentures reduces lateral inferences and occlusal force contacts and direction; thus, LO is theorized to be more suitable for patients with compromised ridges than fully bilateral balanced articulation (FBBA). However, no studies have yet provided evidence to support LO in edentate patients with compromised alveolar ridges. The purpose of this study was to compare LO and FBBA in edentulous individuals with compromised ridges. METHODS: Sixty edentulous individuals were randomly allocated into groups and received dentures with either LO or FBBA. Following delivery, several denture-related satisfaction variables were measured using 100mm visual analogue scales; oral health-related quality of life (OHRQoL) was also assessed using the Oral Health Impact Profile (OHIP). Sub-group analyses of the effect of moderate and severe mandibular bone loss were also carried out. RESULTS: No significant differences were detected between LO and FBBA with the primary outcome. At 6 months, participants with severely atrophied mandibles and FBBA rated their satisfaction with retention of mandibular dentures significantly lower than those with LO (median LO: 86, FBBA: 58.5, p=0.03). They also had significantly lower OHRQoL for the domain of Pain (median LO: 4, FBBA: 5, p=0.02). General satisfaction and total OHIP scores significantly improved between baseline and 6 months only for the LO subjects with severely atrophied mandibles (satisfaction: p=0.003, OHIP total score: p=0.0007). CONCLUSIONS: The results indicate that the LO occlusal scheme with hard resin artificial teeth is more efficient for patients with severely resorbed mandibular ridges.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".