Bibliographic record
Abstract
This editorial refers to ‘Quantitative T-wave analysis predicts 1 year prognosis and benefit from early invasive treatment in the FRISC II study population’† by M.D. Jacobsen et al., on page 112 ‘Proverbial wisdom counsels against risk and change, but sitting ducks fare worst of all’ Mason Cooley An important omission from the report of Jacobsen et al.,1 is a discussion of whether their findings pertain equally to women and men. Since there was a higher frequency of T-wave abnormalities on the admission ECG in women (72% of 749 women vs. 64% of 1708 men, P<0.001) this issue is especially key given that women in the non-invasive arm had a better prognosis than men: moreover, the early invasive strategy employed in FRISC II not only failed to reduce the risk of future events amongst women, but may even have been associated with harm as has been suggested by others.4 In contrast to those with concomitant ST-depression, the T-wave abnormalities, as defined in this study, had no significant prognostic value in the non-invasive assigned group of patients without ST-depression. Are there data within the FRISC II study that might have yielded additional insight into the general applicability of quantitative T-wave analysis? It is now well recognized that quantitative ST-segment analysis provides further partitioning of risk amongst patients with non-ST-elevation acute coronary syndromes and use of this approach might well have attenuated or removed any incremental effect of T-wave analysis given that the ST-depression in this study was defined as ≥0.05 mV in any two contiguous leads and was not further quantitatively analysed.5 It is also surprising that we are not provided with the data acquired from baseline and subsequent cardiac troponins in this population since such measurements have become an accepted component of risk assessment and have recently been shown to provide incremental value over that afforded by quantitative ST-segment analysis alone.6 The assessment of risk in the expanding non-ST-elevation acute coronary syndrome population has become an extraordinarily important point relevant not only to the issue of triage to early interventional therapy but also to the application of a variety of evidence-based therapies.7 The work of Jacobsen et al.1 is welcome in attempting to extend the capacity of simple 12-lead electrocardiography towards this cause. Notwithstanding this however, the panoply of additional biomarkers that have emerged since the FRISC II study was completed will require assimilation in any novel approach to the enhancement of risk assessment in this population. Hence, high sensitivity C-reactive protein, brain natriuretic peptide, and markers of platelet aggregation and thrombosis are appropriate additional contenders.8 In this context, the hypothesis generating observations in the current study are worthy of prospective validation. doi:10.1093/eurheartj/ehi026
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".