Not all diabetic patients were created equal: How to discriminate risk?
Bibliographic record
Abstract
The debate as to the true risk of patients suffering from diabetes mellitus type 2 has been alive since the original publication by Haffner et al. in which the authors suggested that asymptomatic diabetic patients have the same cardiovascular risk of patients from the general population with prior myocardial infarction [1]. The ATP-III embraced this notion and classified all diabetic patients at the highest risk independent of all other markers of peril [2]. But, is it really true that all diabetic patients were created equal? Or is it appropriate to also risk stratify these patients according to a series of discriminating criteria? The article by Yeboah et al. [3] in a previous issue of the journal suggests that the latter may be a more desirable approach than one based on the simple assumption that risk is equal for all patients affected by a certain disease. Indeed, we have seen prior publications in which 15e25% of diabetic patients harbored no subclinical atherosclerosis, as assessed by coronary artery calcium (CAC) screening, and these patients suffered an event rate as low as patients without diabetes [4e7]. On the other hand, presence of coronary calcium and its progression are harbingers of a poor prognosis in diabetes mellitus [5]. However, is the use of cardiovascular imaging truly worth the cost and risk for the
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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 | 0.037 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".