To Exclude or Not To Exclude: Further Examination of the Influence of White Matter Hyperintensities in Diffusion Tensor Imaging Research
Bibliographic record
Abstract
White matter hyperintensities (WMHIs) visible on magnetic resonance imaging (MRI) are common in both healthy adults and in those with medical or psychiatric problems. A practical methodological issue for diffusion tensor imaging (DTI) researchers is whether to include, or exclude, participants from a control group who have WMHIs. The aim of this study was to compare the influence of WMHIs on whole-brain DTI in trauma control subjects. Participants were 48 patients (no-WMHIs, n=36; 2+WMHIs, n=12) prospectively recruited from the Emergency Department of Vancouver General Hospital (British Columbia, Canada). Participants completed an MRI brain scan at 6-8 weeks postinjury (mean, 47.3 days; standard deviation [SD], 6.2; range, 39-66). DTI was used to examine the integrity of white matter (WM) in 50 regions of the brain using measures of fractional anisotropy (FA), and mean (MD), radial (RD), and axial (AD) diffusivity. FA values that were >2 SDs below the mean, and MD, RD, and AD values that were >2 SDs above the mean, were classified as "abnormal scores" indicative of reduced WM integrity. In the entire sample, the 2+WMHI group had a greater number of abnormal FA, MD, and RD scores, compared to the no-WMHI group (p<0.015 and Cohen's d >0.82, indicating large to very large effect sizes, for all comparisons). When controlling for the effects of age using a matched-groups design, the 2+WMHI group still had a significantly greater number of abnormal FA, MD, and RD scores, compared to the no-WMHI group (all p<0.012, all d >0.89, large to very large effect sizes). Researchers should be aware that the inclusion or exclusion of subjects with incidental WMHIs will influence the results of DTI studies.
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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 | 0.065 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".