Sinus augmentation and concomitant implant placement in low bone‐density sites. A retrospective study on an undersized drilling protocol and primary stability
Bibliographic record
Abstract
Abstract Background Recently, a torque‐measuring micromotor has been shown to provide a reliable, quantitative intraoperative evaluation of bone density and implant primary stability. This may be particularly useful for determining bone quality of residual bone and consequently the most appropriate site preparation in the posterior maxilla, where bone often is of low density and quality. Purpose This work aimed to assess the medium‐term success of implants placed during 1‐stage sinus augmentations using an undersized drilling protocol. In case of low bone density, the relationship between residual bone height (RBH) and primary stability was also investigated. Materials and Methods Clinical records of patients who underwent sinus augmentation and concomitant implant placement following a 12.5% undersized drilling protocol were retrospectively analyzed. In all patients, bone density measured intraoperatively was ≤ 0.45 g/cm3. A minimum of 60 months of follow‐up was required for inclusion. Results Records of 106 patients who received 253 implants were reviewed. No significant difference in the implant success rate was found for patients who had less than 4 mm of RBH and those who had more. Conclusions Underpreparation of the implant‐placement sites enabled achievement of successful implant‐supported rehabilitation of the posterior maxilla even when both RBH and bone density were low.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".