Effect of Platform Shift/Switch on Crestal Bone Levels and Mucosal Profile Following Flapless Surgery and Crestal/Subcrestal Implant Placement
Bibliographic record
Abstract
BACKGROUND: Crestal remodeling/bone loss appears a common sequel to dental implant placement. Several hypotheses and clinical strategies have been advanced to explain and avert crestal remodeling; however, causative mechanisms remain unclear and the efficacy of clinical protocol uncertain. OBJECTIVE: The objective of the present study was to provide a histologic record of crestal versus subcrestal implant placement on crestal remodeling and mucosal profile comparing platform shift/switch and standard abutments following flapless implant surgery using a dog model. METHODS: Four dental implants each were placed into the left and right edentulated posterior mandibles in five adult male hound-Labrador mongrel dogs using a flapless approach including crestal versus subcrestal placement and using platform shift versus standard abutments. Block biopsies were collected for histological/histometric analysis following an 8-week healing interval. RESULTS: Both crestal and subcrestal implant installation resulted in significant crestal remodeling and bone loss, in particular at buccal sites, without significant differences between platform shift/switch and standard abutments. Implants installed subcrestally exhibited a significantly taller mucosal profile over crestal-level implants without significant differences between platform shift/switch and standard abutments; the epithelial attachment at all times arrested on the abutment surface. CONCLUSIONS: Comparing platform shift/switch versus standard abutments using a minimally invasive flapless approach including crestal or subcrestal implant placement, the platform shift/switch abutments offer no selective advantage over standard abutments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".