Immediate Implants Placed in Fresh Sockets Associated with Periapical Pathology: A Split‐Mouth Design and Survival Evaluation after 1‐Year Follow‐Up
Bibliographic record
Abstract
PURPOSE: To compare the immediate implant success rates between sites with chronic apical lesions and healthy sites in the same patients 1 year postdelayed loading. MATERIALS AND METHODS: One hundred sixty-eight immediate implants were placed in sixty patients at upper incisor, canine, and premolar sites. A split-mouth design was used, placing a minimum of two implants, one in a fresh socket associated with chronic periapical disease, the average lesion size was larger than 4 mm and less than 8 mm (test group), and the other(s) in a healthy fresh socket (control group). Implant survival rate at 1 year postloading delayed was compared between the groups. RESULTS: The implant survival rate was 98.2% for the total sample (n = 168); out of the three implants lost, two were from the test group, and one was from the control group (in the same patient as one of the former). Among the surviving implants, five were also considered failures due to excessive bone loss (n = 3) and also because of the recurrence of the periapical lesions (n = 2). Survival rates were significantly lower in the test than control sites at 12 months postloading. CONCLUSIONS: Implant survival rates were significantly lower after the immediate implantation in postextraction sockets associated with chronic periapical disease (90.8%) than in healthy postextraction sockets (98.1%).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".