Post‐extraction implant placement into infected versus non‐infected sites: A multicenter retrospective clinical study
Bibliographic record
Abstract
PURPOSE: The aim of this study was to assess whether immediate implant placement into post-extraction sites presenting a chronic infection poses a greater risk of implant failure than immediate placement in non-infected sites. MATERIALS AND METHODS: Records of patients who underwent extraction and immediate implant placement into both infected and non-infected sites from January 1998 to September 2014 at 5 different dental centers were considered for inclusion. Included records were subjected to statistical analysis of survival rates, along with a number of other patient-, implant-, surgery-, and prosthesis-related variables. RESULTS: The inclusion criteria were met by 369 patients who received a total of 527 implants. The follow-up averaged 53.2 months (range 0.9-158.3) for implants placed into non-infected sockets (N = 334) and 50.1 months (range 1.6-146.1) for those placed into infected sites (N = 193). Seven implants failed in non-infected sites and 3 in infected ones. All failures occurred within 1 year of placement. Cumulative implant survival rate for non-infected and infected sites was, respectively, 97.9% ± 0.8% and 98.4% ± 0.9%, being not significantly different (P = .66). None of the investigated variables affected the outcome. CONCLUSIONS: Placement of implants into periodontally or endodontically infected sites immediately after tooth extraction is a safe option, even when the implants are loaded immediately or early.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".