A New Method to Evaluate Volumetric Changes in Sinus Augmentation Procedure
Bibliographic record
Abstract
BACKGROUND: In sinus augmentation procedure, the assessment of volume changes of grafted materials is important both in the clinical practice and in dental research to evaluate the features of filling materials. PURPOSE: In this study, we assessed the repeatability of a new method proposed to evaluate volumetric changes following sinus lift augmentation procedure. MATERIALS AND METHODS: In 10 patients, maxillary sinus augmentation procedure with simultaneous implant placement was performed. Maxillary cone beam computer tomographies were taken 1 week after surgery (T1) and 6 months after surgery (T2). At each evaluation the gap inside the implant between the fixture and the bottom of the screw was used as reference point (Rp), and a standardized volume of interest (VOI) centered on the Rp was selected. Masks were chosen to select the graft and bone tissue within the VOI; the volume at T1, T2, and the difference of volume between T1 and T2 were computed. Expert and non-expert operators performed the analysis. Method errors were computed. RESULTS: The error of the method was 1% for both intra-operator and inter-operator measurements. Tissue contraction at T2 was 19 ± 4% of the total initial volume. CONCLUSIONS: The standardization of the method allows to obtain repeatable measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".