Do Implant- Supported/Retained Prostheses Improve the Quality of Life of Patients with Intraoral Maxillofacial Defects? – A Systematic Review
Bibliographic record
Abstract
BACKGROUND: There is limited evidence available regarding patient satisfaction and quality of life assessment in patients with intraoral maxillofacial defects managed with maxillofacial prostheses. OBJECTIVES: This systematic review aims to understand the impact of intraoral implant prostheses in improving the quality of life in patients with intraoral maxillofacial defects/abnormalities. METHODS: A comprehensive search was performed of nine electronic databases from January 1970 to August 2022. Hand searching of the reference lists of the included papers and of relevant journal publications between 2012 and 2022 was also undertaken. Key information was extracted from included studies alongside quality and risk of bias assessments. RESULTS: The systematic review encompassed a total of seven studies, comprising five retrospective and two prospective investigations, with one of the prospective studies being a randomised clinical trial. The evaluation of the risk of bias and quality assessment revealed heterogeneity in the results, preventing meaningful comparisons among the included studies. CONCLUSION: Within the limitation of the systematic review, there is limited evidence to suggest that implant prostheses improve the quality of life in patients with intraoral maxillofacial defects or abnormalities.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".