Interobserver Reliability in the Classification of Thoracolumbar Fractures Using the AO Spine TL Injury Classification System Among 22 Clinical Experts in Spine Trauma Care
Bibliographic record
Abstract
Study Design Reliability study utilizing 183 injury CT scans by 22 spine trauma experts with assessment of radiographic features, classification of injuries and treatment recommendations. Objectives To assess the reliability of the AOSpine TL Injury Classification System (TLICS) including the categories within the classification and the M1 modifier. Methods Kappa and Intraclass correlation coefficients were produced. Associations of various imaging characteristics (comminution, PLC status) and treatment recommendations were analyzed through regression analysis. Multivariable logistic regression modeling was used for making predictive algorithms. Results Reliability of the AO Spine TLICS at differentiating A3 and A4 injuries (N = 71) (K = .466; 95% CI .458 – .474; P < .001) demonstrated moderate agreement. Similarly, the average intraclass correlation coefficient (ICC) amongst A3 and A4 injuries was excellent (ICC = .934; 95% CI .919 – .947; P < .001) and the ICC between individual measures was moderate (ICC = .403; 95% CI .351 – .461; P < .001). The overall agreement on the utilization of the M1 modifier amongst A3 and A4 injuries was fair (K = .161; 95% CI .151 – .171; P < .001). The ICC for PLC status in A3 and A4 injuries averaged across all measures was excellent (ICC = .936; 95% CI .922 – .949; P < .001). The M1 modifier suggests respondents are nearly 40% more confident that the PLC is injured amongst all injuries. The M1 modifier was employed at a higher frequency as injuries were classified higher in the classification system. Conclusions The reliability of surgeons differentiating between A3 and A4 injuries in the AOSpine TLICS is substantial and the utilization of the M1 modifier occurs more frequently with higher grades in the system.
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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 | 0.040 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".