Do physicians correctly calculate thromboembolic risk scores? A comparison of concordance between manual and computer‐based calculation of <scp>CHADS<sub>2</sub></scp> and <scp>CHA<sub>2</sub>DS<sub>2</sub>‐VASc</scp> scores
Bibliographic record
Abstract
BACKGROUND: Clinical risk scores, CHADS2 and CHA2 DS2 -VASc scores, are the established tools for assessing stroke risk in patients with atrial fibrillation (AF). AIM: The aim of this study is to assess concordance between manual and computer-based calculation of CHADS2 and CHA2 DS2 -VASc scores, as well as to analyse the patient categories using CHADS2 and the potential improvement on stroke risk stratification with CHA2 DS2 -VASc score. METHODS: We linked data from Atrial Fibrillation Spanish registry FANTASIIA. Between June 2013 and March 2014, 1318 consecutive outpatients were recruited. We explore the concordance between manual scoring and computer-based calculation. We compare the distribution of embolic risk of patients using both CHADS2 and CHA2 DS2 -VASc scores RESULTS: The mean age was 73.8 ± 9.4 years, and 758 (57.5%) were male. For CHADS2 score, concordance between manual scoring and computer-based calculation was 92.5%, whereas for CHA2 DS2 -VASc score was 96.4%. In CHADS2 score, 6.37% of patients with AF changed indication on antithrombotic therapy (3.49% of patients with no treatment changed to need antithrombotic treatment and 2.88% of patients otherwise). Using CHA2 DS2 -VASc score, only 0.45% of patients with AF needed to change in the recommendation of antithrombotic therapy. CONCLUSION: We have found a strong concordance between manual and computer-based score calculation of both CHADS2 and CHA2 DS2 -VASc risk scores with minimal changes in anticoagulation recommendations. The use of CHA2 DS2 -VASc score significantly improves classification of AF patients at low and intermediate risk of stroke into higher grade of thromboembolic score. Moreover, CHA2 DS2 -VASc score could identify 'truly low risk' patients compared with CHADS2 score.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".