Implant failures before and after peri‐implantitis surgery: A retrospective study on 207 consecutively treated patients
Bibliographic record
Abstract
PURPOSE: This study aimed to report implant failures before and after peri-implantitis surgery (P-IS) and to compare the pattern of implant failures with in untreated at-risk patients during the same period. MATERIALS AND METHODS: A total of 5628 untreated at-risk patients (7240 jaws) and 207 consecutively patients treated with P-IS (212 jaws) were included in two groups. Implants were placed and followed-up between 1986 and 2018. Cumulative survival rates (CSR) for patients treated with P-IS after 2003 were calculated before and after P-IS and compared with those for nontreated patients. RESULTS: The overall 15-year CSR was 91.2% (CI 95%; 90.5%-91.9%) and 68.5%, (CI 95%, 62.1%-75.5%) for untreated patients at risk and P-IS patients, respectively (P < .05). The 10-year CSR (baseline 1 year after implant surgery) was 97.2% (CI 95%, 95.2%-100%) for treated patients before P-IS which was comparable with that for untreated patients: 95.4% (CI 95%, 94.8%-97.7%). The corresponding 10-year CSR for P-IS patients after surgery was significantly lower (71.6%: CI 95%, 63.1%-81.3% (P < .05)). CONCLUSION: CSR for patients/jaws without implant failures was comparable between untreated and treated P-IS patients before, but lower for P-IS patients after P-IS (P < .05). A negative effect of P-IS on implant survival after treatment cannot be disregarded.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".