Delayed Implants Outcome in Maxillary Molar Region
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to assess bone volume changes in maxillary molar regions after delayed implants placement. MATERIALS AND METHODS: Patients presented large bone defects after tooth extractions. Reactive soft tissue was left into the defects. No grafts were used. Cone beam computed tomography (CBCT) scans were performed before tooth extractions, at implant placement (at 3 months from extraction) and 3 years after implant placement, bone volume measurements were assessed. RESULTS: Bucco-lingual width showed a statistically significant decrease (p = .013) at implant placement, 3 months after extraction. Moreover, a statistically significant increase (p < .01) was measured 3 years after implant placement. No statistically significant differences (p > .05) were found between baseline values (before extraction) and at 3 years from implant placement. Vertical dimension showed no statistically significant differences (p > .05) at implant placement, 3 months after extraction. Statistically significant differences (p < .0001) were found between baseline values (before extraction) and at 3 months from implant placement as well as between implant placement values and 3 years later. CONCLUSION: CT scans presented successful outcome of delayed implants placed in large bone defects at 3-year follow-up.
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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 | 0.001 | 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) | 0.002 | 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".