How predictable are periodontal regenerative procedures?
Bibliographic record
Abstract
Periodontal regeneration has become one of the primary objectives of periodontal therapy. The resulting scientific endeavours have elucidated modes of periodontal wound healing, the growth of periodontal cells and their association with the surrounding matrix, and growth-promoting factors. The periodontal regeneration industry is producing better and more expensive devices, but the criteria for evaluating their success have not progressed to the same extent. Although clinical measurements of attachment level and probing depths, along with radiography, are good methods of evaluating tooth survival and prognosis, they do not indicate true biological regeneration. In addition, the regeneration industry may encourage the overuse of allografts and alloplasts which may serve as an impediment to simple wound healing. This review is a critical assessment of the clinical use of various regenerative tools, specifically bone replacements and membranes. The future of the regeneration industry may depend on the merging of various technologies and biological concepts, including the possible use of biological barriers, various bone and periodontal growth inducers, and artificial matrices that will attract or carry the cells necessary for regeneration.
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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".