Partial Extraction Therapy: A Review of Human Clinical Studies
Bibliographic record
Abstract
Partial extraction therapy (PET) is a collective concept encompassing a group of surgical techniques including socket shield, root membrane, proximal shield, pontic shield, and root submergence. PET uses the patient's own root structure to maintain blood supply derived from the periodontal ligament complex to preserve the periodontium and peri-implant tissues during restorative and implant therapy. This review aims to summarize the current knowledge regarding PET techniques and present a comprehensive evaluation of human clinical studies in the literature. Two independent reviewers conducted electronic and manual searches until January 1, 2021, in the following electronic bibliographic databases: PubMed, EMBASE, and Dentistry & Oral Sciences Source. Gray literature was searched to identify additional candidates for potential inclusion. Articles were screened by a group of 4 reviewers using the Covidence software and synthesized. A systematic search of the literature yielded 5714 results. Sixty-four articles were selected for full-text assessment, of which 42 eligible studies were included in the review. Twelve studies were added to the synthesis after a manual search of the reference lists. A total of 54 studies were examined in this review. In sum, PET techniques offer several clinical advantages: (1) preservation of buccal bone postextraction and limitation of alveolar ridge resorption, (2) mitigation of the need for invasive ridge augmentation procedures, and (3) soft-tissue dimensional stability and high esthetic outcomes. Further randomized clinical studies with larger sample sizes are needed to improve the understanding of the long-term clinical outcomes of PET.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".