Long‐Term Outcome of Implant Rehabilitations in Patients with Systemic Disorders and Smoking Habits: A Retrospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: There is a need for more studies investigating the outcome of implant-supported rehabilitations in patients with systemic disorders. MATERIALS AND METHODS: This retrospective clinical study included 721 patients with systemic disorders or smoking habits (422 females; 299 males), with an average age of 51 years, rehabilitated with 3,998 implants and followed for 3-17 years (average 8 years). Outcome measures were: implant survival rates calculated based on implant function through life tables and using the patient (first implant failure censored) and implant as units of analysis; marginal bone levels measured at 1, 5, and 10 years; and the incidence of biological complications (peri-implant pathology, abscess formation, fistula formation, and suppuration). RESULTS: Eighty-seven patients were lost to follow-up (12%). Forty-five patients experienced prosthetic failure rendering a 94.3% survival rate. One hundred seventy-three implants failed in 98 patients, rendering an 83.5% (patient level) and 94.6% (implant level) cumulative survival rate. The average marginal bone levels were 1.18 mm, 1.56 mm, and 1.47 mm at 1, 5, and 10 years, respectively. Biological complications occurred in 86 patients (11.9%). CONCLUSIONS: Implant rehabilitations in patients presenting systemic disorders or smoking habits are possible with good outcomes. Nevertheless, different impacts on implant rehabilitations were registered according to the type of systemic disorder.
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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) | 0.000 | 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) | 0.001 | 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".