Diagnostic Value of D-Dimer in Acute Myocardial Infarction Among Patients With Suspected Acute Coronary Syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The role of D-dimer as a diagnostic marker in myocardial infarction (MI) and acute coronary syndrome (ACS) is still a question. The aim of this study was to evaluate the diagnostic value of D-dimer in the diagnosis of AMI in patients suspected with ACS. Methods: This cross-sectional study was conducted on patients suspected with ACS. Serial standard 12-lead electrocardiogram (ECG), D-dimer, and troponin tests were done for all the patients. According to the examinations, ECG changes, and troponin, patients were allocated into two groups of MI and unstable angina (UA). Chi-square, independent t -test, and Pearson correlation test were used by SPSS ver, 17. Cut-off point of D-dimer for MI diagnosis was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Seventy-five patients with a mean age of 63.1 ± 9.75 years were studied in two groups of MI (n = 34) and UA (n = 41). Patients were homogeneous based on age, gender, and risk factors for diabetes and dyslipidemia. D-dimer in patients with MI patients was higher than in patients with UA (P = 0.001). The optimal cut-off point of D-dimer for diagnosis of MI was 548 mEq/L with sensitivity and specifity of 63.4% and 91.2%, respectively. Conclusions: Based on the results of this study, it seems that the measurement of D-dimer serum level can be appropriate as a marker with high sensitivity and relatively high specificity for differentiating MI from UA in patients with suspected ACS. Cardiol Res. 2018;9(1):17-21 doi: https://doi.org/10.14740/cr620w Â
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it