The Evaluation Value of Diffusion‐Weighted Imaging for Brain Injury in Patients after Deep Hypothermic Circulatory Arrest
Bibliographic record
Abstract
Objective. Cerebral complications may occur after surgery with deep hypothermic circulatory arrest (DHCA). Diffusion‐weighted imaging (DWI) has shown promising results in detecting early changes of cerebral ischemia. However, studies in human models are limited. Here, we examined the significance of DWI for detecting brain injury in postoperative patients after DHCA. Methods. Twelve patients who had undergone selective cerebral perfusion with DHCA were enrolled. All patients underwent magnetic resonance imaging (MRI) examinations before and after the operation with T1‐weighted phase (T1W) and T2‐weighted phase (T2W). Magnetic resonance angiography (3D TOF) was applied to observe intracranial arterial communication situations. DWI was employed to calculate the apparent diffusion coefficient (ADC) values. The neurocognitive function of patients was assessed preoperatively and postoperatively using the Montreal Cognitive Assessment Scale (MoCA), Hamilton Depression Scale (HAMD), and Hamilton Anxiety Scale (HAMA). Results. The ADC values of the whole brain of patients after surgery were significantly higher than before surgery (P = 0.003). However, no significant difference in the ADC values of other regions before and after the operation was observed. There was no significant effect on the postoperative cognitive function of patients after surgery, but visual‐spatial and executive abilities were significantly reduced, while psychological anxiety (P = 0.005) and depression levels (P < 0.05) significantly increased. Correlation analysis revealed a significant association between ADC change values and depression change values (P < 0.05). Conclusion. DHCA demonstrated no significant effect on the cognitive function of patients but could affect the mood of patients. On the other hand, DWI demonstrated promising efficiency and accuracy in evaluating brain injury after DHCA.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".