Two Million Cycle Fatigue Performance of Custom and Stock Conical‐Hex Abutments: A Removal Torque and <scp>SEM</scp> Study
Bibliographic record
Abstract
ABSTRACT Introduction Screw loosening remains a frequent mechanical complication in implant‐supported prostheses, primarily caused by the gradual loss of abutment‐screw preload. The aim of this study was to evaluate the mechanical performance of CAD‐CAM custom and stock abutments by measuring removal torque values (RTV) at multiple time points and assessing surface morphology by scanning electron microscopy (SEM), following prolonged loading up to 2 × 10 6 cycles. Methods Forty‐four implant‐abutment assemblies with an internal conical‐hex connection were divided into two groups: Stock abutments (SA) and custom abutments (CA). After initial tightening, baseline RTVs were recorded. The samples underwent cyclic loading following ISO‐14801 standards. RTVs were measured after 50 000, 1 × 10 6 , 1.5 × 10 6 and 2 × 10 6 cycles and after post‐fatigue re‐tightening. SEM analysis was performed at baseline, after 1 × 10 6 and 2 × 10 6 cycles. Results Baseline RTVs were higher in SA than CA. Both groups exhibited a progressive decrease in RTVs until 1 × 10 6 cycles, with significantly lower values in the CA. Thereafter, a partial recovery was observed up to 2 × 10 6 cycles, with no significant difference between groups. SEM images revealed more extensive surface wear in the SA group, while the CA group demonstrated localized adaptations; however, thread integrity was maintained in all samples. Conclusion Custom abutments showed lower baseline removal‐torque values yet maintained preload as effectively as stock abutments after two‐million cycles, confirming the mechanical suitability of both designs for functional loading. The greatest preload loss occurred between 50 000 and 1 × 10 6 cycles; therefore, retightening the abutment screws during the early post‐insertion period is recommended to maintain preload stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".