Are acute coronary syndromes risk models too complex? reply
Bibliographic record
Abstract
We thank Drs Gale and Manda for their interest in our study.1 We believe it is important to explore why validated risk scores are often not applied in the ‘real world’, and concur that their perceived complexity may constitute the greatest barrier to more widespread use. However, our findings should not be construed as promoting one risk score over another–rather, our study highlights the important and inevitable tradeoffs between complexity and accuracy. We agree that age and haemodynamic variables are the most powerful prognosticators. Although risk scores incorporating only these variables are purported to be ‘simpler’,2 in reality, their application still requires the use of a calculator and a nomogram for conversion into an estimated risk of adverse events. Thus, it remains unclear whether these ‘simpler’ risk scores are necessarily more user-friendly and less time-consuming, compared with the more ‘sophisticated’ ones. For example, the GRACE risk score calculator, which consists of readily available clinical information, is easy to use, and can be readily downloaded onto a PDA or accessible on the website.3 A major strength of the GRACE risk score is its applicability across the full spectrum of acute coronary syndromes. Because reperfusion therapy should be promptly administered to all patients with ST-elevation myocardial infarction in the absence of contraindications (although the optimal type of reperfusion therapy may depend on clinical presentation and local availability), accurate risk stratification is more relevant in the initial management of non-ST-elevation acute coronary syndrome, which represents a more heterogeneous condition with a variable prognosis. We chose all-cause mortality as our primary study outcome because it was the most robust endpoint. Furthermore, surveillance for myocardial (re-)infarction and the decision to proceed with ‘urgent’ revascularization, especially in the short-term, were probably influenced by physicians' risk assessment. Finally, randomized controlled trials have shown that an early invasive strategy improves long-term outcome.4 Therefore, risk stratification tools that can identify patients with worse long-term outcome are most useful in guiding treatment decisions. Of note, the TIMI risk score demonstrates better discrimination for mortality than the composite endpoint, even in the original derivation cohort.5 Thus, our conclusions appear to be robust and not critically dependent on the chosen endpoint. With respect to the correlations among the risk scores and physicians' assessment, we agree that the highly significant P-values were expected. However, the important point is that there were only weak to moderate correlations―a substantial proportion of patients would be classified into different risk categories, according to these three risk scores and physicians' assessment. This may account for the treatment-risk paradox observed.6 The most important implication of our study is that systematic application of any validated risk score in routine clinical practice will likely improve risk stratification, and consequently, management decisions and patient care. We believe that it is worth ‘taking the trouble’ to apply these risk scores, which can effectively supplement clinical judgment.
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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 | 0.019 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.026 | 0.057 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".