Pilot assessment of a radiologic classification system for segmentation defects of the vertebrae
Bibliographic record
Abstract
Existing nomenclature systems for describing and reporting congenital segmentation defects of the vertebrae (SDV) are confusing, inconsistently applied, and lack molecular genetic advances. Our aim was to develop and assess a new classification system for SDV. A multidisciplinary group of the International Consortium for Vertebral Anomalies and Scoliosis (ICVAS) developed a new classification system for SDV, and 5 members group (Group 1) independently classified 10 previously unseen cases using this system. Inter-observer reliability was assessed using kappa, which compares observed agreement with that expected by chance. Seven independent general radiologists unaffiliated with the ICVAS (Group 2) classified the same 10 cases (total, 70 scores) before and after the ICVAS system was explained. We demonstrated the following: Inter-observer reliability for Group 1 yielded a kappa value of 0.21 (95% confidence intervals (CI) 0.052, 0.366, P = 0.0046); A consensus diagnosis was established for the 10 cases. For Group 2, before the ICVAS system was explained, 1 of 70 scores (1.4%) agreed with the Group 1 consensus diagnoses; Group 2 offered 12 different diagnoses, but 38 of 70 (54.3%) responses were "Don't Know." After the ICVAS system was explained, 47 of 70 responses (67.1%; 95% CI 55.5, 77.0) agreed with the Group 1 consensus, an improvement of 65.7% (95% CI 52.5, 75.6, P < 0.00005), with no "Don't Know" responses. Group 2 average reporting times, before and after explanation of the ICVAS system, were 148 and 48 min, respectively. We conclude that the ICVAS radiological classification system was found to be reliable and applicable for 10 SDV phenotypes.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".