Feasibility of the single‐bolus strategy for measuring the partition coefficient of Gd‐DTPA in patients with myocardial infarction: Independence of image delay time and maturity of scar
Bibliographic record
Abstract
The partition coefficient of Gd-DTPA (lambda) is elevated in infarcted relative to normal myocardium. Although MRI following an infusion of Gd-DTPA allows for the quantification of lambda, infarct imaging is more routinely performed using a bolus. In this study we sought to determine how image delay time and time postinfarction influence the estimation of lambda by the bolus strategy. Both infusion and bolus imaging were performed twice in the same group of patients (N = 9): once at 3-4 weeks and again 6 months after reperfusion therapy for myocardial infarction (MI). Bolus estimates of lambda were compared with those calculated after 60 min infusion, and comparisons were repeated at 6 months. The lambda of infarcted myocardium was significantly greater than that of normal tissue, irrespective of either the technique used or the time postinfarction (P < 0.0001, for each). The concordance (Rc) between bolus and infusion estimates of lambda was >0.83 for all image delays >4 min postinjection, and Rc at 2 min (0.78 +/- 0.04) was significantly less than Rc determined for longer image delay times (P = 0.009). Rc did not change with time postinfarction (P = 0.604). Thus, the bolus strategy can be used to provide estimates of lambda that are stable from 1-6 months postinfarction and independent of image delay time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".