<scp>E</scp>ffect of <scp>D</scp>ental <scp>I</scp>mplant <scp>D</scp>iameter on <scp>F</scp>atigue <scp>P</scp>erformance. <scp>P</scp>art <scp>II</scp>: <scp>F</scp>ailure <scp>A</scp>nalysis
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to perform fracture mode analysis for in vitro failed implants in order to evaluate the relation between the fracture mode obtained and the implants' fatigue behavior. MATERIALS AND METHODS: Eighty fractured dental implants were analyzed after being tested for fatigue performance. A macroscopic failure analysis was performed, which evaluated and located the fracture modes obtained, followed by a microscopic failure analysis comprising a detailed scanning electron microscopy (SEM) fractographic analysis. RESULTS: Four distinctive fracture loci were identified and macrofracture mode analysis was performed, showing that all 5-mm implants that fractured were fractured at the abutment neck and screw. In the 3.75-mm group, 44.4% were fractured at the implant neck and 55.5% at the implants second thread. Fifty-two percent of the 3.3-mm fractured implants had it at the implants second tread and 48% at the implants third thread. The implant's metallographic sections revealed that the different fracture loci were located where thin metal cross sections and sharp notches coexist. Using SEM, we were able to characterize the failure micromechanisms and fatigue characterization as transgranular fracture and arrays of secondary parallel microcracks at relatively low magnifications and classic fatigue striations at much higher magnifications. CONCLUSIONS: The results of this study indicate that proper implant design is crucial to ensure long-term fatigue performance for dental implants. The combination of sharp notches (thread) and narrow metal cross section is quite deleterious for fatigue resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".