Soft Tissue Conditions and Marginal Bone Changes around Immediately Loaded Implants Inserted in Edentate Jaws Following Computer Guided Treatment Planning and Flapless Surgery: A ≥1‐Year Clinical Follow‐Up Study
Bibliographic record
Abstract
BACKGROUND: Evaluation of the clinical conditions following computer guided treatment planning and flapless surgery is still limited. OBJECTIVES: The objective was to evaluate the soft tissue conditions and marginal bone changes after 1 year of function around immediately loaded implants inserted in edentate jaws following computer guided treatment planning and flapless surgery. MATERIAL AND METHODS: Twenty-nine edentate jaws (19 maxillae, 10 mandibles) treated with 165 implants using the Teeth-in-an-Hour™ protocol were included. In these patients, peri-implant soft tissue conditions and radiographic marginal bone changes were evaluated after ≥1 year of functional loading (mean: 19 months). RESULTS: The mean probing depth at case level was 2.6 mm (SD: 0.6). Bleeding on probing was recorded as a mean of 81.9% (SD: 23.0). Plaque index showed a wide range of 0-100%. The mean marginal bone change of measured sites evaluated on intraoral radiographs was -1.2 mm (SD: 1.4). A marginal bone loss more than 1.5 mm or 2.0 mm was observed in 42% and 27% of the measured sites, respectively. A pressure-like-ulcer was found in 9 cases. Implants with marginal bone loss of >1.5 mm were more frequently observed in cases with an ulcer than cases where no ulcer was found. CONCLUSION: Although the mean marginal bone loss after function in the present study was within the range of other reports presenting mean bone loss data after immediate implant loading, our patients showed a wide range of bone loss with several sites, where the bone loss was greater than the commonly used successful level (>1.5 mm).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".