In vitro assessment of a novel additive manufactured titanium implant abutment
Bibliographic record
Abstract
BACKGROUND: Dental implant temporization remains a clinical challenge. A novel abutment simplifying the procedure was developed, but conventional fabrication was costly and unpredictable. A workflow was developed to fabricate the novel abutment using additive manufacturing. This in vitro investigation assessed the additive manufactured (AM) novel abutments to conventionally manufactured (CM) abutments. MATERIAL AND METHODS: The AM abutments were fabricated in dental-grade titanium (Ti-6Al-4V) using Selective Laser Melting and were post-processed. The CM abutments were milled and subsequently laser welded manually. Pin strength of the abutments was assessed using a universal loading machine. Torque was measured by tightening the AM and CM abutments into dental implants within artificial bone. RESULTS: Average pin strength was 364.4 N for the AM abutments and 62.5 N for the CM abutments. Average torque was 49.9 Ncm for the AM abutments and 62.9 Ncm for the CM abutments. AM abutment's pin strength was higher than the CM abutments. CM abutments measured a higher torque than the AM abutments. CONCLUSIONS: Abutment, dental implant, temporization, medical device, prototype design, additive manufacturing, 3D printing, titanium.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".