Marginal bone loss of tissue‐ or bone‐level implants after simultaneous guided bone regeneration in the posterior mandibular region: A retrospective cohort study
Bibliographic record
Abstract
PURPOSE: To analyze the marginal bone loss (ΔMBL) of tissue- or bone-level implants after placed with simultaneous guided bone regeneration (GBR). MATERIALS AND METHODS: A total of 151 patients who received 104 tissue-level or 128 bone-level implants placement with simultaneous GBR in the mandibular posterior region between January 2011 and December 2016 were included in this study. The marginal bone level (MBL) was recorded using the radiographic data obtained at implant placement, second-stage surgery, and the follow-up visit. Generalized estimating equation (GEE) was used to compare the ΔMBL of tissue- and bone-level implants, and the influencing factors of ΔMBL were further analyzed. RESULTS: At the last follow-up visit, the MBL of tissue-level implants was 0.73 ± 0.86 mm, above the rough-smooth interface, while that of bone-level implants was 0.82 ± 1.05 mm, above the implant platform. The ΔMBL of tissue-level implants was 1.03 mm, which was slightly higher than 0.81 mm of bone-level implants, but there was no significant difference (p > 0.05). No contributing factor associated with ΔMBL was identified by multivariate regression analysis in this study. CONCLUSION: Within the limits of this retrospective analysis, the ΔMBL of tissue-level implants is similar to that of bone-level implants after placed with simultaneous GBR, and both types of implants can achieve desirable marginal bone 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".