Associação entre Terapia com Estatinas e Menor Incidência de Hiperglicemia em Pacientes Internados com Síndromes Coronarianas Agudas
Bibliographic record
Abstract
BACKGROUND: Increased risk of new-onset diabetes with statins challenges the long-term safety of this drug class. However, few reports have analyzed this issue during acute coronary syndromes (ACS). OBJECTIVE: To explore the association between early initiation of statin therapy and blood glucose levels in patients admitted with ACS. METHODS: This was a retrospective analysis of patients hospitalized with ACS. Statin-naïve patients were included and divided according to their use or not of statins within the first 24 hours of hospitalization. The primary endpoint was incidence of in-hospital hyperglycemia (defined as peak blood glucose > 200 mg/dL). Multivariable linear and logistic regression models were used to adjust for confounders, and a propensity-score matching model was developed to further compare both groups of interest. A p-value of less than 0.05 was considered statistically significant. RESULTS: A total of 2,357 patients were included, 1,704 of them allocated in the statin group and 653 in the non-statin group. After adjustments, statin use in the first 24 hours was associated with a lower incidence of in-hospital hyperglycemia (adjusted OR=0.61, 95% CI 0.46-0.80; p < 0.001) and lower need for insulin therapy (adjusted OR = 0.56, 95% CI 0.41-0.76; p < 0.001). These associations remained similar in the propensity-score matching models, as well as after several sensitivity analyses, such as after excluding patients who developed cardiogenic shock, severe infection or who died during index-hospitalization. CONCLUSIONS: Among statin-naïve patients admitted with ACS, early statin therapy was independently associated with lower incidence of in-hospital hyperglycemia. (Arq Bras Cardiol. 2021; 116(2):285-294).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".