Clinical Outcomes of Implants Placed in Extraction Sockets and Immediately Restored: A 7‐Year Single‐Cohort Prospective Study
Bibliographic record
Abstract
BACKGROUND: The placement of implants immediately after tooth extraction has proven to be a predictable treatment strategy with a very high success rate. PURPOSE: The aim of the present 7-year prospective single cohort study was to evaluate the success rate, marginal bone level (MBL), soft tissue stability of implants placed in fresh extraction sockets and immediately restored. MATERIAL AND METHODS: This prospective cohort study included 37 implants in 32 patients (19 females and 13 males) with an average age of 40.1 ± 13.3 (range: 21-63 years) who received immediate implants and immediate single unit restorations. Outcome evaluations were: implant failures, complications, MBL, width of keratinized gingiva, facial soft tissue (FST) levels, modified Plaque Index and modified Bleeding Index. RESULTS AND CONCLUSIONS: The cumulative survival rate was of 94.6% at 7-year visit. The mean MBL was -0.6 ± 0.49 mm at baseline and 1 ± 0.2 mm after 7 years. The FST Level was 0.4 ± 0.69 mm at baseline and 0.02 ± 0.70 mm at the 7-year follow-up. The Width of Keratinazed Gingiva was 3.8 ± 0.47 mm at baseline and 3.1 ± 0.42 mm at 7-year follow-up. Implants placed immediately after tooth extraction and immediately restored showed predictable clinical outcomes in this prospective study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".