Bibliographic record
Abstract
Myron Nevins, DDS: The success and predictability of the science of osseointegration has had a global impact on dental treatment planning. P.-I. Brånemarks 1984 presentation in Toronto opened the door to a new thought process that has influenced everyday decisions in patient care. My introduction to dental implants in 1966 was with subperiosteal implants that were mainly effective for atrophic edentulous mandibles. This era also included blade implants, but these implants were found to have a significant lack of predictability. In the mid-1980s, implant practitioners began to expand beyond treating edentulous patients to include partially dentate individuals. Someone with a single missing tooth could have a single restoration without depreciating the structure of adjacent teeth. It was immediately apparent that partial restorations could not display titanium abutments such as those that were acceptable for mandibular edentulism. This necessitated innovative approaches and resulted in customized abutments to bring about results simulating fixed restorative dentistry. Patient acceptance and satisfaction were immediately enhanced. Two significant detriments to patient care remained. The first was the 3- to 6-month waiting period between implant placement and delivery of the final prosthesis. A publication by Lazzara demonstrating success with an implant placed in an extraction socket played a significant role in legitimizing a shorter treatment regime. Today many patients are restored with provisional and sometimes permanent restorations delivered simultaneously with implant placement. Free-hand surgery has been supplemented by computer-guided implant placement. A second detriment was the lack of alveolar process and the presence of anatomical obstacles such as the inferior alveolar nerve and maxillary sinus. The 1990s ushered in a rapid progression of surgical procedures with the use of biologics to construct bone that would successfully support occlusal loads. This was further augmented by the introduction of growth factors at the beginning of the 21st century. The understanding of the role played by the implant surface became more and more sophisticated. This has improved success in challenging situations and resulted in developments in which rougher surfaces can be used more apically and smoother surfaces more occlusally. In the future, I fully expect to see a steady stream of additional breakthroughs and improvements involving implants, abutments, impression techniques, and restorative materials that will be a segue for continued success. This special supplemental issue reports on a number of recent developments aiming to make implant treatment safer, more effective, and enduringly esthetic. I truly expect the best is yet to be.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".