Distribution and prognosis of acute ischaemic stroke with negative diffusion-weighted imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To examine the distribution and prognosis among patients with diffusion-weighted imaging (DWI)-negative acute ischaemic stroke (AIS) and explore the differences between mild (National Institute of Health Stroke Scale (NIHSS) score ≤5) and major (NIHSS score >5) ischaemic stroke in DWI-negative patients. METHODS: Patients with AIS with baseline DWI from the Third China National Stroke Registry (CNSR-III), based on a prospective, observational, multicentre cohort study, between August 2015 and March 2018, were included. Patients were classified into negative and positive DWI groups depending on the existence of acute lesions on DWI. RESULTS: Among 12 026 patients who had an ischaemic stroke, 932 (7.7%) had negative DWI. As the NIHSS score increased, the proportion of patients with DWI-negative AIS gradually decreased. Negative DWI was associated with a decreased risk of stroke recurrence (HR 0.63, 95% CI 0.49 to 0.82), combined vascular events (HR 0.72, 95% CI 0.56 to 0.92), mortality (HR 0.60, 95% CI 0.36 to 0.995) and poor functional outcomes (OR 0.57, 95% CI 0.43 to 0.76) within 1 year in all patients. Similar associations were observed in patients with mild AIS (p<0.05 for all) but not in patients with major AIS, excluding poor functional outcomes (OR 0.47, 95% CI 0.28 to 0.81). CONCLUSIONS: The proportion of patients with DWI-negative AIS gradually decreased as the NIHSS score increased, and negative DWI was mainly observed in patients with mild AIS. Negative DWI was associated with a better prognosis for all patients with AIS. However, these associations were significant for mild AIS but not for those with major AIS.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".