<scp>Long‐term</scp>results of implants and i<scp>mplant‐supported</scp>prostheses under systematic supportive implant therapy: A retrospective<scp>25‐year</scp>study
Bibliographic record
Abstract
BACKGROUND: Long-term data (>10 years) concerning the survival and success rates of implants and implant-supported prostheses are scarce. PURPOSE: The present investigation represents one of the first studies on dental implants covering an observational period of 25 years. MATERIALS AND METHODS: This study presents the results obtained in 26 patients with 75 implants who participated over a 23- to 28-year period in a supportive implant therapy (SIT) program at a private dental practice. We extracted existing data from the patients' files (pocket depths [PDs], bleeding on probing [BoP], radiographic peri-implant bone loss, and survival rates of the implant-supported prostheses). RESULTS: After 25 years, the SIT-compliant patients' implants had a survival rate of 95% (prostheses: 88%). The mean peri-implant probing depth was 3.69 mm (median: 3.33; SD: 1.06; range: 2-8.33). The mean peri-implant bone level was 1.84 mm (median: 1.82; SD: 1.20; range: -0.97-5.2). Finally, the prevalence (moment of last consultation) and incidence (during the entire observational period) of peri-implantitis were 7% and 30%, respectively. CONCLUSIONS: Under SIT conditions, clinicians may expect survival rates for implant-supported prostheses of >80%. Most implants (60%) did not develop signs of peri-implantitis over a 25-year period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".