Patient‐Reported Healing of Static Computer‐Assisted Sinus Lateral Window Osteotomy: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: The primary aim was to investigate differences in patient-reported healing outcomes between static computer-assisted sinus floor augmentation (SCA-SFA) and the conventional freehand approach (SFA). MATERIAL AND METHODS: Patient-reported healing outcomes were recorded in visual analog scale (VAS) on days 1-7 and 14, and intra and postoperative complications were assessed on weeks 2 and 4 after surgery. Operation time and operators' assessment of efficacy for SCA-SFA utility were recorded. Independent t-tests and Chi-square exact tests were performed for statistical evaluation between groups. RESULTS: Forty patients underwent lateral sinus augmentation (20 freehand-SFA + 20 SCA-SFA). No statistically significant difference was found between the two groups with regard to PROMs and intra, postoperative complications, apart from a higher level of swelling for SCA-SFA patients on day 2 after surgery (p = 0.04). The use of SCA-SFA significantly reduced the time needed to conduct the window osteotomy (SFA 18.56 ± 12.17 min vs. SCA-SFA 11.43 ± 4.75 min; p = 0.022) and the total surgery duration (SFA 69.89 ± 20.08 min vs. SCA-SFA 56.24 ± 16.01 min; p = 0.023). CONCLUSIONS: Within the limitations of the study, SCA-SFA should be preferred when the reduction of surgical time is a priority while the costs of the intervention do not play a major role, and the design of the surgical guide should strive for minimal invasiveness. CLINICAL TRIAL REGISTRY: TCTR20230427005.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".