Validation of syncope short-term outcomes prediction by machine learning models in an Italian emergency department cohort
Bibliographic record
Abstract
Machine learning (ML) algorithms have the potential to enhance the prediction of adverse outcomes in patients with syncope. Recently, gradient boosting (GB) and logistic regression (LR) models have been applied to predict these outcomes following a syncope episode, using the Canadian Syncope Risk Score (CSRS) predictors. This study aims to externally validate these models and compare their performance with novel models. We included all consecutive non-low-risk patients evaluated in the emergency department for syncope between 2015 and 2017 at six Italian hospitals. The GB and LR models were trained and tested using previously validated CSRS predictors. Additionally, recently developed deep learning (TabPFN) and large language models (TabLLM) were validated on the same cohort. The area under the curve (AUC), Matthews correlation coefficient (MCC), and Brier score (BS) were compared for each model. A total of 257 patients were enrolled, with a median age of 71 years. Thirteen percent had adverse outcomes at 30 days. The GB model achieved the best performance, with an AUC of 0.78, an MCC of 0.36, and a BS of 0.42. Significant performance differences were observed compared with the TabPFN model (p < 0.01) and the TabLLM model (p = 0.01). The GB model performed only slightly better than the LR model. The predictive capability of the GB and LR models using CSRS variables was reduced when validated in an external syncope cohort characterized by a higher event rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".