Human Cadaver Study Evaluating a New Measurement Technique for Graft Volumes after Sinus Floor Elevation
Bibliographic record
Abstract
PURPOSE: Volumetric data can be used as complementary information to characterize grafting materials. The aim of this cadaveric study was to analyze a noncommercial measurement technique based on the novel concept of an "interactive rigid registration algorithm" (IRRA). Parameters analyzed included the reproducibility of IRRA measurements and their reliability in comparison with the established measurement technique of "region growing segmentation thresholding" (RGST). MATERIALS AND METHODS: Three human skulls were used to simulate a total of 18 sinus grafts, using three incremental grafting procedures in each sinus (three skulls ×t wo sinuses × three grafting increments). Radiopaque impression material was used for the simulated grafts, whose volumes were recorded by computed tomography from three different tilt angles. The reproducibility of IRRA measurements and the reliability of volumetric results obtained with both the IRRA and RGST techniques were evaluated by appropriate intraclass correlation coefficient (ICC) and Bland-Altman analysis. RESULTS: ICC greater than 0.9 indicated close to perfect agreement of the results obtained with both methods and good reproducibility of the IRRA measurements. Bland-Altman analysis demonstrated good inter-method and intramethod agreement. CONCLUSIONS: The IRRA measurement technique can be recommended as a noninvasive tool to evaluate graft volumes in human maxillary sinuses.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".