Performance of the “CCS Algorithm”\n\t\t\t\t in real world patients
Bibliographic record
Abstract
With the publication of the 2014 Focused Update of the Canadian Cardiovascular Society Guidelines for the Management of Atrial Fibrillation, the Canadian Cardiovascular Society Atrial Fibrillation Guidelines Committee has introduced a new triage and management algorithm, the so-called “CCS Algorithm”. The CCS Algorithm is based upon expert opinion of the best available evidence, however, the CCS Algorithm has not yet been validated. Accordingly, the purpose of this study is to evaluate the performance of the CCS Algorithm in a cohort of real world patients. We compared the CCS Algorithm with the European Society of Cardiology (ESC) Algorithm in 172 hospital inpatients who are at risk of stroke due to non-valvular atrial fibrillation in whom anticoagulant therapy was being considered. The CCS Algorithm and the ESC Algorithm were concordant in 170/172 patients (99% of the time). There were two patients (1%) with vascular disease, but no other thromboembolic risk factors, which were classified as requiring oral anticoagulant therapy using the ESC Algorithm, but for whom ASA was recommended by the CCS Algorithm. The CCS Algorithm appears to be unnecessarily complicated in so far as it does not appear to provide any additional discriminatory value above and beyond the use of the ESC Algorithm, and its use could result in under treatment of patients, specifically female patients with vascular disease, whose real risk of stroke has been understated by the Guidelines.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".