Failures in Fixed Dental Prostheses: A Clinical Survey on Causes and Longevity
Bibliographic record
Abstract
Crown and bridge failures are considered one of the most frequent challenges that dentists deal with on a daily basis. The aim of the current study was to assess the causes of failures in fixed dental prostheses in patients reporting to the dental clinics at Ahram Canadian University, Cairo, Egypt. Using a comprehensive clinical survey, causes of failures and serviceability patterns of the fixed dental prostheses (FDP) were explored in a sample of 80 patients (mean age 39.7±11) visiting the clinic for complications related to FDP. Patients were clinically and radiographically examined, and all symptoms related to their complaints were recorded. Descriptive analysis was performed, and SPSS version 22 was used for statistics. Results indicated that most of the failures were mechanical in nature (30%), followed by biological failures (20%), and then aesthetics reasons (11%). 45.7 % of the FDP served for 1-5 years, 28.6 % served for 6-12 months, 14.30% served for more than 5 years, while the least numbers of prostheses (11.4%) served for less than 6 months. Proper patient selection, diagnosis, and treatment planning is essential to maintain and increase the longevity of FDP. It is also crucial to educate patients about good dental hygiene and prosthesis maintenance as well as for the dentist to fully recognize the disadvantages of each dental material to prevent the problems they induce and be able to make the best decision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".