Autogenous particulated dentin for alveolar ridge preservation. A systematic review
Bibliographic record
Abstract
PURPOSE: This review aimed to investigate the clinical outcomes of autogenous particulated dentin (APD) used for alveolar ridge preservation (ARP), evaluating volume gain, histologic/histomorphometric data, and associated complications. MATERIAL AND METHODS: The review followed PRISMA guidelines and was registered in the International Prospective Register of Systematic Reviews (PROSPERO). An automated search was made in four databases (Medline/Pubmed, Scopus, Web of Science, and Cochrane Library) supplemented by a manual search for relevant clinical articles published before March 10th, 2022. The review included human studies of at least four patients in which extraction and subsequent ARP were performed in a single surgery. Both comparative studies and studies that assessed ARP with APD exclusively were admitted. The quality of evidence was assessed with the Cochrane bias assessment tool, the Newcastle-Ottawa Quality Assessment Scale, and the Joanna Briggs Institute Critical Appraisal tool. RESULTS: Eleven studies fulfilled the inclusion criteria and were included for descriptive analysis, with a total of 215 patients, and 337 alveoli preserved by APD, spontaneous healing (blood clot), or other bone substitutes, obtaining comparatively less vertical and horizontal resorption when APD was used. CONCLUSIONS: After dental extraction, autogenous dentin was effective in terms of volume maintenance, showing promising results in histologic/histomorphometric analysis, and a low complication rate. Nevertheless, few comparative studies with comparable parameters have been published and so more research providing long-term data is needed to confirm these findings.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".