External validation of Leipzig-Halifax scores for aortic dissection in Armenia
Bibliographic record
Abstract
Background Few prognostic tools are currently available to predict hospital mortality in patients with acute type A aortic dissection. The aim of this study was to validate the performance of two existing risk-assessment tools, the original and the adjusted Leipzig-Halifax scorecards, to predict hospital mortality among Armenian patients with acute type A aortic dissection. Methods This retrospective cohort study included all consecutive patients with acute type A aortic dissection who were admitted to two tertiary cardiac centers in Armenia and underwent surgery from January 2008 to April 2018. We evaluated the predictive power of the original and adjusted Leipzig-Halifax scorecards using logistic regression analysis. Results Overall, 211 patients (76% males, mean age 57 ± 9 years) were included in the study, of whom 37 (17.5%) died during hospitalization. The adjusted Leipzig-Halifax score, but not the original Leipzig-Halifax score, was a significant predictor of hospital mortality. Patients with medium and high adjusted Leipzig-Halifax scores had a significantly higher odds of death compared to patients with low scores (odds ratio = 3.0 vs. 3.9, 95% confidence interval: 1.3–6.9 vs. 1.0–14.9, respectively). The areas under the receiver operating characteristic curves were 0.58 and 0.63, respectively, p > 0.05. Conclusion The adjusted Leipzig-Halifax score performed slightly better than the original Leipzig-Halifax score in the Armenian acute type A aortic dissection population. The adjusted Leipzig-Halifax score should now be applied prospectively to generate more data for further validation and potential improvement.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".