Intra- and Interobserver Reliability of Bone Volume Estimation Using OsiriX Software in Patients with Cleft Lip and Palate Using Cone Beam Computed Tomography
Bibliographic record
Abstract
The objective of the current study was to evaluate intra- and interobserver bone volume measurements and effect of orientation on the reliability of bone volume evaluation in as-acquired vis-à-vis oriented cone beam computed tomography scans using Osirix software and possible correlation between gender, age, and bone volume required. For this, 31 cone beam computed tomography (CBCT) scans of 14 girls and 17 boys (aged 9–13) with unilateral cleft lip and/or palate who met the inclusion criteria were analyzed. Efficacy and reliability of third party software Osirix for bone volume calculation was assessed using as acquired and oriented volumes by three specialists (a radiologist, an orthodontist, and an oral maxillofacial surgeon). The dataset and readings were anonymized to prevent any bias. Two-way mixed model analysis on as-acquired and oriented observations exhibited intra-class coefficient (ICC) values ≥ 0.90. Wilcoxon signed rank test (p = 0.10) and Kruskal–Wallis ANOVA (p = 0.46) indicated that although a clinical difference in volume assessment was seen between as-acquired and oriented observations (inter-observer and intra-observer), it was statistically insignificant. Spearman’s bi-variate correlation analysis revealed a significant relation between the type (side) of cleft and bone volume required to fill the defect (p < 0.05). Although there was clinical difference in bone volume measurement by the three observers, it was insignificant statistically. Clefts on the left side in the patients had significantly more bone required than the right side, whereas age and gender had no relation with bone needed to fill the defect. OsiriX software provided good reliability in measurements of bone volume.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".