Factors Affecting Survival and Usefulness of Implants Placed in Vascularized Free Composite Grafts Used in Post–Head and Neck Cancer Reconstruction
Bibliographic record
Abstract
BACKGROUND: Bone-containing vascularized grafts have been used successfully to reconstruct post-cancer surgical defects. Dental implants can be placed in these bone-containing grafts to allow implant-supported prosthodontic reconstruction of these patients. PURPOSE: The aim of this study was to evaluate the survival of dental implants used in the rehabilitation of subjects treated with bone-containing vascularized grafts to compare usability of implants placed at the time of reconstruction and after healing. MATERIALS AND METHODS: A cross-sectional study was undertaken to examine survival rates of implants placed in vascularized bone-containing grafts either immediately at the time of surgical reconstruction or after 3 months healing. Other factors such as graft type, whether radiation therapy was given, and implant type were recorded. RESULTS: A total of 41 patients had 145 implants placed in 47 vascularized bone-containing flaps. Increased failure rate of implants was seen in immediately placed implants. There was also a significant increase in the number of osseointegrated implants that were prosthodontically unusable or sub-optimally placed in the immediate placement group. Radiation therapy was associated with a significant increase in failure rate. Modern implant surfaces appeared to perform better than machined/turned surfaces. Graft donor site did not influence implant survival. CONCLUSION: This study demonstrated the difficulties encountered with immediate placement of dental implants at the time of post-cancer reconstructive surgery.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".