Feasibility and safety of continuous glucose monitoring systems in acute myocardial infarction subjects undergoing primary percutaneous coronary interventions
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the feasibility and safety of continuous glucose monitoring systems (CGMS) in ST segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary interventions (p-PCI) in coronary care units (CCU). METHODS: CGMS was performed for 3 days during CCU hospitalization for each of the subjects. The correlation between glucose values, recorded with CGMS, and finger-stick capillary glucose values was examined. The parameters and safety of CGMS were also investigated. RESULTS: Data from 219 subjects were included in the statistical analysis. Correlation analysis showed a strong positive correlation between interstitial glucose values recorded by CGMS and the corresponding capillary glucose values (P<0.001). The daytime mean blood glucose (MBG), the nighttime MBG and PT7.8 were the highest in the first day of CGMS compared with the second and third day. Furthermore, there were no indications of major hemorrhage or hematoma at the site of sensor insertion. Any adverse events were mild. CONCLUSIONS: CGMS glucose values are relatively accurate and reliable. CGMS were safe and can be used as a tool to detect trends in glucose levels and to predict upcoming glucose excursions in STEMI patients undergoing p-PCI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".