Validation of the AOSpine-DGOU Osteoporotic Fracture Classification – Effect of Surgical Experience, Surgical Specialty, Work-Setting and Trauma Center Level on Reliability and Reproducibility
Bibliographic record
Abstract
Study DesignCross-sectional survey.ObjectivesA cornerstone of classification systems is good reliability amongst different groups of classification users. Thus, the aim of this international validation study was to assess the reliability of the new AO Spine DGOU Osteoporotic Fracture Classification (OF classification) stratified by surgical specialty, work-setting, work-experience, and trauma center level.Methods320 spine surgeons were asked to rate 27 cases according to the OF classification at 2 time points, 4 weeks apart (assessment 1 and 2) in this online-webinar based validation process. The kappa statistic (κ) was calculated to assess the inter-observer reliability and the intra-rater reproducibility.ResultsA total of 7798 (90.3%) ratings were recorded in assessment 1 and 6621 (76.6%) ratings in assessment 2. Global inter-rater reliability was moderate in both assessments (κ = 0.57; κ = 0.58). Participants with a work-experience of >20 years showed the highest inter-rater agreement in both assessments globally (κ = 0.65; κ = 0.67). Participants from a level-1 trauma center showed the highest agreement (κ = 0.58), whereas participants working at a tertiary trauma center showed higher grade of agreement in the second assessment (κ = 0.66). Participants working in academia showed the highest agreement in assessment 2 (κ = 0.6). Surgeons with academic background and surgeons employed by a hospital showed substantial intra-rater agreement in the second assessment.ConclusionsThe AO Spine-DGOU Osteoporotic Fracture Classification showed moderate to substantial inter-rater agreement as well as intra-rater reproducibility regardless of work-setting, surgical experience, level of trauma center and surgical specialty.
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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 | 0.069 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".