Long‐term radiographic assessment of titanium implants installed in maxillary areas grafted with autogenous bone blocks using two predefined sets of success criteria
Bibliographic record
Abstract
PURPOSE: Assess the radiographic peri-implant bone loss of implants installed in maxillary areas grafted with autogenous bone and classify the long-term (at least >4 ≤ 6 years) implant success according to two predefined sets of criteria. MATERIAL AND METHODS: Sixty patients had full maxillary alveolar reconstructions using autogenous bone grafts (iliac crest), and 369 titanium implants were installed. The follow-up protocol was 5 (>4 ≤ 6) years; thereafter only patients who presented significant peri-implant bone loss were followed up to 12 years. The radiographic peri-implant bone level was assessed on panoramic radiographs in relation to the baseline and used to classify the long-term success of the implants according to the predefined success criteria presented by Albrektsson and coworkers (ALB; 1986) and the Pisa Consensus Conference (PCC; 2007). RESULTS: Fifteen implants were lost over the 12-year follow-up period (two up to >4 ≤ 6 years). Mean radiographic peri-implant bone loss was 2.7 mm at the >4 ≤ 6 years control and 4.2 mm after >11 ≤ 12 years. Different success criteria resulted in different types of prevalence of implants classified as "failures." At >4 ≤ 6 years, 48% of the implants would be "failures" according to ALB, while according to the PCC, only 0.8% would be "failures" and 18.1% would be classified as "compromised survival" and 44.8% as "satisfactory survival." CONCLUSIONS: Mean peri-implant bone loss of implants installed in maxillary areas grafted with autogenous bone blocks was 2.7 mm after >4 ≤ 6 years, and two implants were lost during this period. The use of different success criteria significantly altered the prevalence of implants classified as "failure."
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".