Maxillary sinus augmentation with three different biomaterials: Histological, histomorphometric, clinical, and patient‐reported outcomes from a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Lateral maxillary sinus augmentation (MSA) is a predictable bone regeneration technique in case of atrophy of the posterior-upper maxilla. Aimed at obtaining quantity and quality of bone suitable for receiving osseointegrated implants, its success is largely due to the skill of the surgeon, but also to the characteristics of the biomaterial used. METHODS: Twenty-four patients needing MSA were included in the study. The patients were randomly allocated to three different groups: anorganic bovine bone mineral as control, tricalcium phosphate with or without hyaluronic acid (HA) as test groups. Nine months after MSA, bone biopsies were harvested for the histomorphometric analysis. Secondary outcomes were mean bone gain, intraoperative and postoperative complications, implant insertion torque, implant failure, and patient-reported outcome measures. RESULTS: Although the percentage of new bone was not statistically different between the three groups (P = .191), the percentages of residual biomaterial was significantly higher (P < .000) and nonmineralized tissue significantly lower (P < .000) in the control than in the test groups. Test groups did not differ significantly from each other for all histomorphometric parameters. The implant insertion torque was significantly higher in the control group (P < .0005). The rest of the secondary outcomes were not significantly different between the groups. CONCLUSION: MSA is a safe and predictable procedure in terms of histological, clinical, and PROAMs, regardless of the biomaterial used. The addition of HA did not influence the outcomes.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".