Retrospective review of in hospital use of mineralocorticoid receptor antagonists for high risk patients following myocardial infarction
Bibliographic record
Abstract
BACKGROUND: There is little data regarding use of mineralocorticoid antagonists (MRAs) for patients reduced LV ejection fraction (LVEF) following acute myocardial infarction (MI). We determined the frequency and temporal trends of MRA use in these patients. METHODS: We performed a retrospective review of all cases of acute MI between June 1, 2010 and April 1, 2012. Patients were considered eligible for MRA therapy if they were admitted with acute MI with LVEF ≤ 40 % and had heart failure symptoms or a history of diabetes. RESULTS: Of 3910 cases of acute MI, 332 patients were considered eligible for MRA therapy. MRA therapy was prescribed for 92/332 (28 %) eligible patients, while 66 of 1142 (6 %) of ineligible patients were so treated. Over the study period, usage in eligible and ineligible patients rose significantly (22 to 30 %, p = 0.08 and 4 to 7 %, p = 0.04 respectively). CONCLUSIONS: Prescription of MRAs for eligible patients occurred in a minority of patients, and demonstrated a modest increase over time. In patients without an indication for MRAs, a similar trend was observed. Further study is required to better understand barriers to appropriate use of MRAs in this patient population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".