Comparison of the prognosis of the remaining teeth between implant‐supported fixed prostheses and removable partial dentures in partially edentulous patients: A retrospective study
Bibliographic record
Abstract
BACKGROUND: There have been several reports about the prognosis of teeth adjacent to edentulous spaces for implant-supported fixed prostheses (ISFPs) and removable partial dentures (RPDs). However, there are few reports about the prognosis of the other remaining teeth comparing ISFPs with RPDs. PURPOSE: The aim of this study was to evaluate and compare the prognosis of the remaining teeth for ISFPs and RPDs in terms of survival and complication-free rates. METHODS: Subjects were partially edentulous patients with ISFPs or RPDs inserted in 2003-2016. Teeth adjacent to edentulous spaces (A-teeth), teeth not adjacent to edentulous spaces (R-teeth), and teeth opposing edentulous spaces (O-teeth) were investigated. The endpoints were tooth extraction and complications. A multivariate cox regression model was used to estimate the risk factors for survival of the investigated teeth. RESULTS: A total of 233 (ISFP: 89, RPD: 144) patients were included in the statistical analyses. An IFSP prosthesis, when compared to an RPD prosthesis did not significantly decrease the tooth loss rate for A-teeth (hazard ratio [HR]: 0.76; 95% confidence interval [CI]: 0.30-1.92), for R-teeth (HR: 0.54; 95% CI: 0.28-1.05), or for O-teeth (HR: 0.45; 95% CI: 0.10-2.09). CONCLUSIONS: In partially edentulous spaces, the difference between ISFPs and RPDs does not affect the prognosis of teeth adjacent to edentulous spaces, teeth not adjacent to edentulous spaces, and teeth opposing edentulous spaces. Namely, our findings suggest that it depends largely on the tooth type, jaw, endodontic therapy performed, not on the type of prostheses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".