Evaluating SWAG and Its Validity When Compared to 3D Imagery of Secondarily Grafted Cleft Sites
Bibliographic record
Abstract
Objective To test validity of 2D Standardized Way to Assess Grafts (SWAG) ratings to assess 3D outcomes of bone grafting (ABG). Patients 43 patients (34 UCLP, 9 BCLP) with non-syndromic complete clefts, bone-grafted at mean age 9yrs/3mos, with available post-graft occlusal radiographs and cone beam computed tomography (CBCT) (taken mean 4yrs/9mos post-ABG). Main Outcome Measures 2D occlusal radiographs rated twice using SWAG by 6 calibrated raters. 12 scores were averaged and converted to a percentage reflecting bone-fill. Weighted Kappas were assessed for SWAG reliability. 3D cleft-site bone volume was calculated by 1 rater using ITK-SNAP. 13 cleft sites were re-measured by the ‘one rater’ for 3D reliability using Intraclass Correlation Coefficient (ICC). 2D versus 3D ratings were compared using paired t-test, independent samples t-test, Bland-Altman and Linear Regression. Significance level was P = .5. Results 2D reliability was 0.724 (intra-rater) and 0.546 (inter-rater). 3D reliability was 0.986. Bland-Altman plot comparing 2D vs 3D showed for 45 of 47 graft-sites were within 2 SD's. Mean % bone-fill was 64.11% with 2D and 69.06% with 3D (mean difference = 4.95%) that was a non-significant difference in both t-tests. Regression showed a statistically significant relation between the two methods (r 2 = 0.46; P = .0001). Conclusion 2D SWAG systematically and non-significantly underestimated bone-fill. There was a significant correlation between 2D/3D methods. Bland-Altman analysis illustrated the similarity of the two methods. For comparisons of group (cleft treatment Centers’) bone grafting outcomes, the 2D method may suffice as a proxy for the 3D method. However, with individual variation up to 40% in 2D estimates of actual 3D volume, 2D SWAG method cannot be used in place of 3D images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".