Performance of six bone collectors designed for dental implant surgery
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to perform an in vitro comparison of six bone collectors for harvesting of particulate bone. MATERIAL AND METHODS: Four commercially available bone collectors (Frios, Osseous Coagulum Trap, ACE Autografter, Bone Trap) and two custom-designed models were tested. Three different in vitro tests were performed to determine the harvesting capabilities of the collectors. In test I, a bovine mandible was drilled and the bone collectors were used to collect bone chips. The harvested bone volumes and dry weights were measured after harvesting. In test II, three dental implant sites were prepared in a bovine mandible. The bones from the implant osteotomies were collected, and bone volumes and dry weights were measured. In test III, 1 ml of bone chips was mixed with water, and suctioned through the bone collectors. The volumes of the bone chips retained were measured to determine the efficiency of each collector. RESULTS: The Osseous Coagulum Trap and the custom-made collectors were the most effective instruments in test I. The mean volumes ranged from 0.17 to 0.38 ml. In test II, the difference between the collectors was small and the bone volume ranged from 0.28 to 0.37 ml. In test III, the Bone Trap became blocked before the other collectors, and its bone procurement was therefore limited. CONCLUSION: Comparison of six different bone collectors in this in vitro study showed that all collectors are usable in clinical situations but their effectiveness varies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".