Red cell and platelet distribution widths in patients with angina pectoris and acute myocardial infarction
Bibliographic record
Abstract
Aim: We aimed to determine the relationship of red cell and platelet distribution widths with the onset of acute myocardial infarction, to enable the early detection and prevention of acute myocardial infarction. Methods: Red cell and platelet distribution widths were retrospectively determined in 46 patients with stable angina pectoris and 140 patients with acute myocardial infarction who were brought to the emergency department of our institution. Red cell and platelet distribution widths were determined with an automatic blood cell analyzer, and the results were compared between the acute myocardial infarction and angina pectoris groups. Results: Both red cell and platelet distribution width values obtained at onset were significantly higher in the acute myocardial infarction group than in the angina pectoris group (red cell distribution widths, 46.4 ± 0.51% versus 44.5 ± 0.59%; mean difference -1.91 [95% confidence interval (CI), -3.79 to -0.34]; platelet distribution widths, 12.1 ± 0.22 fL versus 11.1 ± 0.17 fL; mean difference -1.03 [95% CI, -1.58 to -0.47]). The red cell distribution widths before onset was not different between the acute myocardial infarction and angina pectoris groups; however, the platelet distribution widths before onset was higher in the acute myocardial infarction group (red cell distribution widths, 46.5 ± 0.85% versus 45.9 ± 0.59%; mean difference -0.71 [95% CI, -2.74 to 1.30]; platelet distribution widths, 11.4 ± 0.39 fL versus 10.6 ± 0.21 fL; mean difference -0.83 [95% CI, -1.66 to 0.11]). Conclusion: Red cell distribution widths and especially platelet distribution widths may contribute to the early detection of acute myocardial infarction.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".