Classification of Pelvic Fractures: Analysis of Inter- and Intraobserver Variability Using the Young-Burgess and Tile Classification Systems
Bibliographic record
Abstract
Classification systems for pelvic ring injuries have been developed to assist in understanding the anatomy of the injury, predicting prognosis, and helping define treatment. Despite the frequent clinical use of the Young-Burgess and Tile classification systems, to our knowledge little work has been conducted to validate either system. We assessed the degree of inter- and intraobserver variability when using both the Young-Burgess and Tile classification systems and thereby assessed their validity for clinical use. Eighty-nine isolated pelvic ring disruptions were selected. Sets of injury images were randomly ordered and distributed to 5 orthopedic trauma surgeons blinded to the patients' names, attending surgeons, dates of injury, and eventual treatments. The surgeons were asked to independently classify each pelvic ring disruption based on the Young-Burgess and Tile classifications. Eight weeks later, the same images were randomly ordered and redistributed to the same 5 surgeons, who were again asked to classify the pelvic injuries. A kappa analysis was conducted to analyze agreement among surgeons. A moderate degree of agreement was shown among orthopedic trauma surgeons when using both the Young-Burgess and Tile classification systems. Intraobserver agreement was found to be substantial for the Young-Burgess classification and moderate for the Tile classification. The degree of inter- and intraobserver variability may limit the usefulness of the 2 classification systems, both clinically and for research purposes.
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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 | 0.043 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".