Strategies for Optimizing the Soft Tissue Seal around Osseointegrated Implants
Bibliographic record
Abstract
Percutaneous and permucosal devices such as catheters, infusion pumps, orthopedic, and dental implants are commonly used in medical treatments. However, these useful devices breach the soft tissue barrier that protects the body from the outer environment, and thus increase bacterial infections resulting in morbidity and mortality. Such associated infections can be prevented if these devices are effectively integrated with the surrounding soft tissue, and thus creating a strong seal from the surrounding environment. However, so far, there are no percutaneous/permucosal medical devices able to prevent infection by achieving strong integration at the soft tissue-device interface. This review gives an insight into the current status of research into soft tissue-implant interface and the challenges associated with these interfaces. Biological soft/hard tissue interfaces may provide insights toward engineering better soft tissue interfaces around percutaneous devices. In this review, focus is put on the history and current findings as well as recent progress of the strategies aiming to develop a strong soft tissue seal around osseointegrated implants, such as orthopedic and dental implants.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".