Assessing Dental Implant Success: A Systematic Review and Meta‐Analysis of Primary Versus Secondary Implantation in Free Bone Flap Reconstruction for Malignant Tumors
Bibliographic record
Abstract
BACKGROUND: Dental implantation of bone reconstructions in oncologic situations improves patients' orofacial function and quality of life. There are currently no recommendations on the timing of implantation. METHODS: This systematic review with meta-analysis aimed to compare primary and secondary dental implantation of free bone flaps in reconstructions for malignant tumors of the oral cavity. The primary objective was to evaluate the implant survival rate, with secondary criteria including time to prosthesis placement, postoperative complications, and data on quality of life. Results-Three databases were screened for articles published between January 1990 and April 2024. Out of 2438 studies, 16 met the eligibility criteria, encompassing 284 patients. In total, 319 implants were placed in the primary implantation group and 1108 in the second group. Implant survival rate was 92.5% in the primary implantation group compared to 88.5% in the secondary implantation group. This systematic review underscores a higher success rate for implants placed primarily in patients with oral cancer. CONCLUSIONS: Given the rapid functional and aesthetic improvement offered by prosthetic rehabilitation, primary implantation should be systematically considered in the oncological population.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".