Multidimensional 3D-Printed Scaffolds and Regeneration of Intrabony Periodontal Defects: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The utilization of regenerative techniques in periodontology involves tailoring tissue engineering principles to suit the oral cavity's unique environment. Advancements in computer-assisted technology, specifically utilizing cone beam computed tomography (CBCT), enabled the fabrication of 3D-printed scaffolds. The current review aims to explore whether 3D-printed scaffolds are effective in promoting osteogenesis in patients with periodontal defects. METHODS: A thorough exploration was undertaken across seven electronic databases (PubMed, Scopus, ScienceDirect, Google Scholar, Cochrane, Web of Science, Ovid) to detect pertinent research in accordance with specified eligibility criteria, aligning with the PRISMA guidelines. Two independent reviewers undertook the screening and selection of manuscripts, executed data extraction, and evaluated the bias risk using the Newcastle-Ottawa Scale for non-randomized clinical trials and SYRCLE's risk of bias tool for animal studies. RESULTS: Initially, 799 articles were identified, refined by removing duplicates. After evaluating 471 articles based on title and abstract, 18 studies remained for full-text assessment. Eventually, merely two manuscripts fulfilled all the eligibility criteria concerning human trials. Both studies were prospective non-randomized clinical trials. Moreover, 11 animal studies were also included. CONCLUSIONS: The use of multidimensional, 3D-printed, customized scaffolds appears to stimulate periodontal regeneration. While the reported results are encouraging, additional studies are required to identify the ideal characteristics of the 3D scaffold to be used in the regeneration of periodontal tissue.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it