Performance of zirconium abutments from different implant designs in esthetic areas
Bibliographic record
Abstract
T advantages of zirconia implant abutments are enhanced esthetics with less gingival grey-blue discoloration than titanium abutments and enhanced biocompatibility. Dental zirconia (Y-TZP) is becoming the ceramic material of choice for implant abutments, especially in esthetic areas. Nevertheless, most of the data presented to date for zirconia abutments is for the standard platform implants. This lecture will focus on the esthetic parameters of zirconia abutments in implant dentistry. One of the studies that will be thoroughly discussed evaluated the standard and platform switching implant-supported restorations as well as different implant designs. This investigation was conducted to assess complications, survival and success rates of zirconia abutments for implant-supported single crowns in esthetic zones. The peri-implant parameters were observed as well as mechanical complications, such as loss of retention and presence or absence of abutment fractures. The pros and cons of zirconia standard platform abutment designs compared to zirconia platform switching abutments will be presented in light of the current available knowledge.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".