Assessment of antegrade/retrograde cardioplegia by magnetic resonance technique
Bibliographic record
Abstract
目的 应用磁共振技术评估相同流量下顺、逆行心肌灌注的效果.方法应用磁共振成像比较顺、逆行灌注离体猪心期间的T2*或T1信号曲线的相关性,用磷31磁共振图谱比较两种灌注方式对心肌能量代谢的影响.结果逆行灌注期间各T2*信号曲线的相关性(0.52±0.15)明显弱于顺行灌注(0.84±0.07).同样,逆灌期间各T1信号曲线的相关性(0.49±0.09)也明显弱于顺灌(0.86±0.12).而且逆行灌注需要比顺行灌注更高的流量才能维持正常能量代谢.结论与顺行灌注相比,逆行灌注时局部心肌灌注明显不均匀,而且逆灌维持正常能量代谢的能力也明显弱于顺灌。
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".