Key CT markers for predicting haemorrhagic transformation after ischaemic stroke: a prospective cohort study in China
Bibliographic record
Abstract
OBJECTIVES: Limited studies have systematically addressed the CT markers of predicting haemorrhagic transformation (HT). We aimed to (1) investigate the predictive ability of the imaging factors on multimodal CT for HT and (2) identify the key CT markers that can accurately predict HT while maintaining easy and rapid assessment in the early stage of stroke. DESIGN AND SETTING: This was a prospective cohort study conducted in a tertiary hospital in Southwest China. PARTICIPANTS: Patients with ischaemic stroke admitted within 24 hours after onset were included. OUTCOME MEASURES: The primary outcome was measured as the overall HT. The secondary outcomes were the presence of parenchymal haematoma, symptomatic HT and spontaneous HT. RESULTS: A total of 763 patients were included. The early hypodensity >1/3 of the middle cerebral artery (MCA) territory, Alberta Stroke Programme Early CT Score≤7, midline shift, hyperdense middle cerebral artery sign (HMCAS), poor collateral circulation, infarct core and penumbra was independently associated with the increased risk of HT (all p < 0.05). The sensitivity of midline shift for predicting HT was only 3.5%, whereas its specificity was 99.8%. The combination of the early hypodensity >1/3 of the MCA territory, midline shift and HMCAS showed a good predictive performance for HT (area under the curve 0.80, 95% CI 0.75 to 0.84). CONCLUSIONS: Seven imaging factors on multimodal CT were independently associated with HT. The high specificity of midline shift suggests the need to consider it as an imaging indicator when assessing the risk of HT. The early hypodensity >1/3 of the MCA territory, midline shift and HMCAS was identified as the key CT markers for the early prediction of HT. The coexistence of the three key factors might be a valuable index for identifying individuals at high bleeding risk and guiding further treatments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".