MGUS Predicts Worse Prognosis in Patients with Coronary Artery Disease
Bibliographic record
Abstract
We performed a retrospective cohort study to analyze all 87 CAD patients with MGUS and 178 CAD patients without MGUS admitted in Zhongshan Hospital Fudan University from 2015 to 2017. Patients were followed up via regular patient visits or telephone, and the median follow-up period was 2.9 years. The end point of follow-up was the occurrence of major adverse cardiac events (MACE). CAD patients with MGUS had a higher risk of MACE than those without MGUS (log-rank P = 0.0015). After adjustment for other markers in the stepwise Cox regression model, MGUS was still related to the increasing risk of MACE incident (P = 0.002, HR = 2.308). Then, we constructed the nomogram based on the Cox regression model, and the concordance index (C-index) was 0.667. Hence, MGUS might be added into the risk model of CAD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".