Augmentation of Exposed Implant Threads with Autogenous Bone Chips: Prospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: Autogenous bone chips can be harvested during drilling of implant sites and may be used as a graft material for bone augmentation and coverage of exposed implant threads. PURPOSE: The aim of this prospective study was to evaluate the possibility of augmenting exposed implant threads with autogenous bone chips. MATERIALS AND METHODS: Twenty-one consecutive patients treated with screw-shaped oral implants with exposed threads due to buccal fenestration or marginal defects were augmented with autogenous bone harvested with a bone trap during drilling of the implant site. Both marginal (9 sites) and fenestration defects (12 sites), with 4 to 14 exposed implant threads, were registered clinically and with photography. The number of exposed implant threads was measured before and at second-stage surgery 6 months after augmentation. RESULTS: Complete bone coverage of the exposed implant threads was seen in 12 of the 21 implant sites. Six sites showed one to two remaining exposed threads, two showed about 40% coverage, and one showed flattening of the defect but with eight of nine exposed threads at 6 months follow-up. The mean bone gain was 81% in patients with a marginal defect and 82% in patients with a fenestration defect. CONCLUSION: The results from this clinical study show that it is possible to gain bone over exposed implant threads by augmentation with autogenous bone chips.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".