Implant‐supported mandibular removable partial dentures: Functional, clinical and radiographical parameters in relation to implant position
Bibliographic record
Abstract
BACKGROUND: Patients with a Kennedy class I situation often encounter problems with their removable partial denture (RPD). PURPOSE: To assess the functional benefits of implant support to RPDs, the clinical performance of the implants and teeth and to determine the most favorable implant position: the premolar (PM) or molar (M) region. MATERIALS AND METHODS: Thirty subjects received 2 PM and 2 M implants. A new RPD was made. Implant support was provided 3 months later. In a cross-over model, randomly, 2 implants (PM or M) supported the RPD during 3 months. Masticatory performance was assessed using the mixing ability index (MAI). Clinical and radiographic parameters were assessed. Non-parametric statistical analysis for related samples and post hoc comparisons were performed. RESULTS: Masticatory performance differed significantly between the stages of treatment (P < .001). MAI-scores improved with implant support although the implant position had no significant effect. No complications to the implants or RPD were observed and clinical and radiographical parameters for both implants and teeth were favorable. Higher scores for bleeding on probing were seen for molar implants. CONCLUSIONS: Implant support to a Kennedy class I RPD significantly improves masticatory function, regardless of implant position. No major clinical problems were observed.
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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 | 0.001 | 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".