Minimally Invasive Approaches in Endodontic Practice by Gianluca Plotino, Editor. Cham, Switzerland: Sringer Nature; 2021
Bibliographic record
Abstract
A new must-read textbook, Minimally Invasive Approaches in Endodontic Practice, is designed by top experts for the future generation of specialists in endodontics. The textbook consists of 9 beautifully-written Chapters carefully prepared by 18 co-authors from 11 countries (Brazil, Canada, France, Greece, Hong Kong [the Special Administrative Region, China], Italy, Norway, Portugal, Spain, United States of America, and Venezuela) under the leadership of Gianluca Plotino (Italy). For the practitioners who are only starting to grow or already deeply specialize in endodontic microsurgery, the Chapter 7, “Minimally Invasive Approach to Endodontic Retreatment and Surgical Endodontics” by Mario Zuolo (São Paulo, Brazil) and Leandro Pereira (Campinas, Brazil) is the useful one. The chapter highlights important data in a very informative academic way. The table 7.1 is more than worth of attention due to the state of the art comparison of technical differences between macro- and microsurgery. The apical microsurgery success rate of around 90 percent comparing to less than 60 percent of macrosurgery clearly shows the advantages of the first one. Perfect illustration of retrocavity filing (i.e., retrofilling) is a role model part of protocol of the microsurgical management for the apical root region. In summary, this textbook is a very important source of knowledge and practical skills for every dental practitioner related more or less with the endodontics.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".