The Potential Risk of Conical Implant‐Abutment Connections: The Antirotational Ability of <scp>C</scp>owell Implant System
Bibliographic record
Abstract
BACKGROUND: Conical implant-abutment connections are popular because of good antibending performance. However, the cross section is round, and the antirotational ability is questionable because restorations in the oral cavity also have to bear torsional moments resulting from chewing patterns. PURPOSE: The purpose of this study was to investigate the antirotational ability of conical implant-abutment connections with and without an index. MATERIALS AND METHODS: Conical connection implant system (Cowell Medi, Busan, South Korea) was selected. Two kinds of cyclic loading, a bending moment with (C) and without (L) a torsional moment, were respectively applied to two kinds of abutments, pure cone (N-Octa) and cone with an octagonal index (Octa). The number of cycles to fatigue and the failure modes was recorded. Morphologies of the abutments were examined with scanning electron microscopy. RESULTS: Only group C(N-Octa) passed the fatigue test, whereas the other three groups failed because of different failure modes. In group L(N-Octa), all abutments generated rotation within 150 cycles. In groups C(Octa) and L(Octa), all abutments fractured but in different areas. CONCLUSIONS: In Cowell implant system (taper angle = 7°), there was no antirotational ability in purely conical connections. Adding an octagonal index could provide an antirotational function but could compromise the antibending strength of the abutment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".