White Matter Hyperintensity Volume Influences Symptoms in Patients Presenting With Minor Neurological Deficits
Bibliographic record
Abstract
Background and Purpose— Acute minor neurological deficits are a common complaint in the emergency department and differentiation of transient ischemic attack/minor stroke from a stroke mimic is difficult. We sought to assess the ability of white matter hyperintensity (WMH) volume to aid the diagnosis in such patients. Methods— This is a post hoc analysis of the previously published SpecTRA study (Spectrometry in TIA Rapid Assessment) of adult patients that presented to the emergency department with acute minor neurological deficits between December 2013 and March 2017. WMH volumes were measured if fluid-attenuated inversion recovery imaging was available. Outcomes of interest were final diagnosis, symptoms at presentation, and 90-day stroke recurrence. Results— WMH volume was available for 1485 patients. Median age was 70 years (interquartile range, 59–80), and 46.7% were female. Mean WMH volume was higher in transient ischemic attack/minor strokes compared with stroke mimics (1.71 ln mL [95% CI, 1.63–1.79 ln mL] versus 1.15 ln mL [95% CI, 1.02–1.27 ln mL], P <0.001). In multivariable-adjusted logistic regression analysis, WMH volume was not associated with final diagnosis. However, the combination of both diffusion-weighted imaging positivity and high WMH volume led to lower odds of focal symptoms at presentation ( P =0.035). Conclusions— The combination of diffusion-weighted imaging positivity and high WMH volume was associated with lower odds of focal symptoms at presentation in patients seen with minor neurological deficits in the emergency department. This suggests that WMH volume might be an important consideration and the absence of focal symptoms at presentation should not discourage clinicians from further investigating patients with suspected cerebral ischemia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".