Maxillary Sinus Augmentation with Autogenous Tooth Grafting Material: A Systematic Review
Bibliographic record
Abstract
The aim of this systematic review was to determine whether autogenous tooth grafting material (ATGM) is as safe and effective as other bone substitutes used for maxillary sinus augmentation procedures, evaluating histomorphometric and/or histological data, implant primary stability, associated complications and radiographic bone height measurements. An automated electronic search was conducted using four databases (Medline/PubMed, Scopus, Web of Science and Cochrane Library), supplemented by a manual search, to identify clinical human studies using particulate ATGM for the aforementioned procedure. The included studies had a sample size of at least four patients and were published before 31st July 2024. The Newcastle-Ottawa scale (NOS) and Joanna Briggs Institute (JBI) Critical Appraisal Checklist were used to assess the risk of bias in cohort studies and case series, respectively. Seven studies were included in the descriptive analysis, obtaining 128 participants (46.8% only treated with ATGM) and 192 placed implants. Due to the heterogeneity of the studies, meta-analysis could not be performed. The authors concluded that ATGM appears to be a feasible and safe alternative for maxillary sinus augmentation procedures. These results should be interpreted with caution due to the limited amount of scientific evidence on this topic and the heterogeneity between the included studies.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".