Longitudinal study of carbon monoxide intoxication by diffusion tensor imaging with neurospsychiatric correlation
Bibliographic record
Abstract
BACKGROUND: White matter damage is common after carbon monoxide (CO) intoxication, but in vivo follow-up studies about the mechanism of white matter damage are not possible in pathology series. Diffusion tensor imaging (DTI) and voxel-based morphometry (VBM) can quantify diffusion parameters and volumetric changes in white matter that can be correlated with neuropsychological performances in longitudinal studies. METHODS: We examined 9 patients with CO intoxication using DTI, VBM and neuropsychologic tests at an average of 3 and 10 months after CO exposure. We used data from 18 age- and sex-matched controls for comparison. RESULTS: We found that cognitive recovery at 10 months after CO intoxication was not significant, although it was after 3 months. The neuropsychologic tests correlated better for the fibre tract of the semicentrum ovale and not the periventricular fibres. Diffusion measures suggest increases in fractional anisotropy, mean diffusivity and axial eigenvalues over time, while increases in radial eigenvalue were evident at 3 months compared with controls. Periventricular white matter atrophy was observed 10 months after CO intoxication. LIMITATIONS: Our study included few cases, and the interpretation of the putative changes on neuroimaging findings cannot be confirmed by histology. CONCLUSION: Our study showed that the evolution of white matter injury in CO encephalopathy occurred over time. Cognitive recovery was not evident in the follow-up period because of white matter injuries.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".