Flapless Implant Surgery Using a Mini‐Incision
Bibliographic record
Abstract
BACKGROUND: Traditional flapless implant surgery using a soft tissue punch device requires a circumferential excision of keratinized tissue at the implant site. A new flapless implant technique that can submerge implant fixtures is needed. PURPOSE: This article describes a flapless implant surgery method using a mini-incision and compares the effects of soft tissue punch and mini-incision surgery on both the amount of osseointegration and the bone height around the implants using a canine mandible model. MATERIALS AND METHODS: Bilateral, edentulated, flat alveolar ridges were created in the mandibles of six mongrel dogs. After a 3-month healing period, two implants were placed on each side of the mandible using either soft tissue punch or mini-incision procedures. After an additional 3-month healing period, a second stage surgery and transmucosal abutment attachment was performed for mini-incision implant cases. Following a 2-month healing period, the dogs were sacrificed to evaluate the osseointegration and bone height around the implants. RESULTS: Average bone height was 9.6 ± 0.4 mm in the soft tissue punch group and 9.8 ± 0.3 mm in the mini-incision group (p > .05). Average osseointegration was 70.4 ± 6.3% in the soft tissue punch group and 71.2 ± 7.1% in the mini-incision group (p > .05). No significant differences were noted between the two groups in vertical alveolar ridge height or bone/implant contact. CONCLUSIONS: Our findings support the clinical use of mini-incision implant surgery at sites where implants need to be protected below the soft tissue during the early phase of healing, particularly for patients with poor bone quality and/or low primary implant stability.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".