Immediate Loading of Post-Extraction Implants: Success and Survival Rates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The study aimed to assess the success and survival rates of post-extraction implants with immediate loading. A systematic search was performed in the Medline (PubMed), Cochrane Library (Wiley), and Scopus (Elsevier) databases to identify randomized and non-randomized studies of intervention (NRSI) on bone loss and success and survival rates in post-extraction implants with immediate, early, and delayed loading. Articles were selected based on predefined inclusion and exclusion criteria, and the risk of bias was assessed following Cochrane guidelines, and the Newcastle–Ottawa Scale was used to assess quality. A fixed-effect meta-analysis was conducted to evaluate bone loss using mean, median, and standard deviation, and to calculate odds ratios for success-survival rates. Of the 13 studies identified, three met the criteria for inclusion in the meta-analysis, involving a total of 178 patients and 296 post-extraction implants. Bone loss was the lowest in the delayed loading group (0.51 mm) compared to immediate (0.55 mm) and early loading (0.54 mm). Implant failures were similar in the immediate and early groups (one case each), while two failures were reported in the delayed group. Delayed loading reduced peri-implant bone loss, but immediate loading showed a slightly higher success-survival rate. Further high-quality studies are needed to strengthen the evidence on the long-term effects of the different implant loading protocols.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".