Autogenous Demineralized Dentin Graft With High Molecular Weight Hyaluronic Acid in Ridge Preservation: Pilot Trial
Bibliographic record
Abstract
OBJECTIVE: The current trial assessed for the first time radiographic and histological alterations, following alveolar ridge preservation (ARP), using autogenous demineralized dentin graft carried in 0.2% high molecular weight sodium hyaluronate (ADDG+HA; test group) versus autogenous demineralized dentin graft (ADDG, control group) alone. MATERIAL AND METHODS: Thirty patients (n = 30) with non-restorable single-rooted teeth were randomly assigned into two groups (n = 15/group). Following extraction, ARP was performed using either ADDG solely or ADDG+HA. Bucco-lingual alveolar ridge width (BLRW; primary outcome), buccal (BRH) and lingual ridge height (LRH), percentage of newly formed bone, soft tissue and residual graft in human biopsies histologically, as well as patients' pain and discomfort (all secondary outcomes) were assessed after 6 months at the time of implant placement. Sample bone core biopsies were further collected, processed, and histomorphometrically and SEM analyzed. RESULTS: For the ADDG and ADDG+HA groups, the alveolar ridge dimensional changes were comparable, being -1.21 ± 0.77 mm and -1.18 ± 0.86 mm in BLRW, -0.89 ± 0.74 and -0.83 ± 0.85 mm in BRH, and -0.9 ± 0.76 mm and -1.05 ± 1.18 mm in LRH respectively (p > 0.05). Clinically, no complications, pain, or inflammatory responses were reported. Histologically, all samples demonstrated bone growth and socket bone fill, while the ADDG+HA group showed a significantly greater presence of mineralized mature bone, which accounted for 33% ± 8.1% of the specimen after 6 months. CONCLUSIONS: Both ADDG and ADDG+HA demonstrated comparable outcomes in terms of ARP. HA amalgamation with ADDG appears to enhance bone mineralization and maturation, yet without a significant impact on dimensional changes during ARP procedures. TRIAL REGISTRATION: NCT05613075.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".