Bibliographic record
Abstract
The oral health of children is a cornerstone of overall well being, with proper dental care playing a pivotal role in ensuring healthy growth and development. Among the various aspects of pediatric dentistry, restoring and preserving primary and young permanent teeth is a significant challenge faced by dental practitioners. Crowns in Pediatric Dentistry aims to provide a comprehensive, evidence-based resource on the art and science of restoring decayed, fractured, or malformed teeth in children using crowns. This book is designed to serve as a guide for dental students, practitioners, and specialists, offering practical insights into the selection, design, and placement of crowns. It covers the fundamental principles of crown therapy, the unique considerations in pediatric patients, and the latest advancements in materials and techniques. Whether addressing primary teeth restoration, aesthetic challenges, or managing patient behavior, the text bridges the gap between theoretical knowledge and clinical practice. In writing this book, our goal is to empower clinicians with the tools to provide effective, durable, and patient-friendly restorations. By emphasizing a child-centered approach, we hope to instill confidence in professionals to deliver care that aligns with the needs and expectations of children and their families.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".