# Vertebra Level Fracture Recognition on Private Dataset Source: [Vertebra Level Fracture Recognition on Private Dataset]() Collection index: [llms.txt]() Data updated: 2026-02-26T06:00:28.331Z Evaluation of model performance for Vertebra Level Fracture Recognition using the Private Dataset, reporting metrics such as ROC-AUC and Sensitivity. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Vertebra Level Fracture Recognition | | Dataset (including any stated split) | Private Dataset | | Metric | ROC-AUC | | Unit | SCALAR | | Direction | Higher is better | | Results loaded | 2 of 2 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [ROC-AUC](): Higher is better; unit: SCALAR. - [Sensitivity](): Higher is better; unit: SCALAR. ## Recorded results | Method | ROC-AUC | Result date | Report paper | | --- | --- | --- | --- | | Nicolaes et al. | 0.93 | Not recorded | 6971af3ae25ad2fa9d1edc13 | ### Nicolaes et al. ROC-AUC: 0.93. Result ID: 69979d2f5698665ec4f31686. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Test Set Size":"90 Patients","Cross Validation":"true","Major Drawback":"Dataset was small"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.