Abstract 8: Early MRI in TIA And Minor Stroke: Do it or Lose it
Bibliographic record
Abstract
Background: MRI is not always completed early after TIA or minor stroke and this may affect its utility. We measured the impact of scanning an individual patient late versus early in the investigation of TIA and minor stroke. Methods: 263 patients with a TIA or minor stroke (NIHSS <4) from the CATCH study were included in this analysis. To be included in this sub study patients needed to have had a baseline MRI completed within 24 hours of symptom onset and a follow-up MRI at 90 days. All MRI images were acquired on a 3.0 Tesla GE scanner. Baseline and 90 day scans were assessed independently for the presence of any stroke lesion. The presence and pattern of any stroke lesion was then compared at the two time points. Lesion patterns were classified as: no definite stroke, single territory cortical stroke(s), multiple territory cortical strokes, single territory subcortical only stroke(s), multiple territory subcortical only strokes, and multiple strokes in one territory including a cortical stroke Results: Stroke of any age, in any location was more common on the baseline MRI versus 90day MRI (68% versus 58%, p=0.005). A substantial proportion of the negative scans at 90 days had a clearly identifiable stroke on the baseline scan (35/115: 30%) that was missed on the 90day scan. All of these lesions were acute or subacute DWI lesions on the baseline scan showing non-specific white matter hyperintensity or no abnormality on the 90day scan. Among 104 patients with a stroke lesion on the 90 day MRI considered as a cause for the presenting symptoms, this lesion was the correct lesion in only 78 (53%) patients. 89 (34%) patients had a different lesion pattern on the baseline scan versus the 90day scan. The main difference observed was that patients with multiple DWI lesions on the baseline scan were either seen as a single or no lesions on the 90day MRI. Conclusion: Completing an MRI in a delayed fashion after TIA or minor stroke reduces the diagnostic yield of the imaging. Not only does it reduce lesion detection, but also the pattern of the lesions is missed. Conclusions regarding the original event may be false if based only on a delayed MRI. If minor stroke and TIA patients are going to be scanned with MRI this should be completed early after symptom onset.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".