[Correlation between diffusion tensor imaging and cognitive dysfunction in patients with delayed encephalopathy after acute carbon monoxide poisoning].
Bibliographic record
Abstract
OBJECTIVE: To explore the diagnostic value of magnetic resonance (MR) diffusion tensor imaging (DTI) in detecting brain white matter (WM) damage of patients with delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) and evaluating their cognitive dysfunction. METHODS: Thirteen patients with DEACMP and thirteen age- and sex-matched volunteers underwent DTI using 1.5T MR scanner. FA and ADC values of 16 WM regions of interests (ROIs) were measured on DTI by two experienced radiologists independently with double blind methods, cognitive functions were evaluated by another experienced neurologist blinded to patient's medical history using the Montreal cognitive assessment (MoCA). ADC and FA values in DEACMP patients, and their correlations with cognitive dysfunction were analyzed. RESULTS: ADC values of DEACMP patients increased significantly in all ROIs (P < 0.05) in comparison with the corresponding ROIs of healthy controls, whereas FA values were significantly decreased in all ROIs (P < 0.05) in comparison with that in controls except the bilateral optic radiations, anterior and posterior internal capsules. MoCA scores were positively correlated with FA values of bilateral lower frontal (r(L) = 0.736, P = 0.011; r(R) = 0.762, P = 0.003) lobe, temporal lobe (r(L) = 0.605, P = 0.016; r(R) = 0.559, P = 0.021) and total average WM (r(A) = 0.688, P = 0.001), however it inversely correlated with ADC values of bilateral lower frontal WM (r(L) = -0.674, P = 0.007; r(R) = -0.681, P = 0.019). CONCLUSION: DTI can quantitatively reveal WM microstructure damage of DEACMP patients, indicate the severity of cognitive dysfunctions, and provide important information for pathogenesis and pathological study for DEACMP.
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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".