Polymeric Scaffolds for Dental, Oral, and Craniofacial Regenerative Medicine
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
Dental, oral, and craniofacial (DOC) regenerative medicine aims to repair or regenerate DOC tissues including teeth, dental pulp, periodontal tissues, salivary gland, temporomandibular joint (TMJ), hard (bone, cartilage), and soft (muscle, nerve, skin) tissues of the craniofacial complex. Polymeric materials have a broad range of applications in biomedical engineering and regenerative medicine functioning as tissue engineering scaffolds, carriers for cell-based therapies, and biomedical devices for delivery of drugs and biologics. The focus of this review is to discuss the properties and clinical indications of polymeric scaffold materials and extracellular matrix technologies for DOC regenerative medicine. More specifically, this review outlines the key properties, advantages and drawbacks of natural polymers including alginate, cellulose, chitosan, silk, collagen, gelatin, fibrin, laminin, decellularized extracellular matrix, and hyaluronic acid, as well as synthetic polymers including polylactic acid (PLA), polyglycolic acid (PGA), polycaprolactone (PCL), poly (ethylene glycol) (PEG), and Zwitterionic polymers. This review highlights key clinical applications of polymeric scaffolding materials to repair and/or regenerate various DOC tissues. Particularly, polymeric materials used in clinical procedures are discussed including alveolar ridge preservation, vertical and horizontal ridge augmentation, maxillary sinus augmentation, TMJ reconstruction, periodontal regeneration, periodontal/peri-implant plastic surgery, regenerative endodontics. In addition, polymeric scaffolds application in whole tooth and salivary gland regeneration are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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