Possible Influence of Ethnicity on Computed Tomography Perfusion Parameter Thresholds in Acute Ischaemic Stroke
Bibliographic record
Abstract
INTRODUCTION: Tissue at risk, as estimated by CT perfusion utilizing Tmax+6, correlates with final infarct volume (FIV) in acute ischaemic stroke (AIS) without reperfusion. Tmax thresholds are derived from Western ethnic populations but not from ethnic Asian populations. We aimed to investigate the influence of ethnicity on Tmax thresholds. METHODS: From a clinical-imaging registry of Australian and Indonesian stroke patients, we selected a participant subgroup with the following inclusion criteria: AIS under 24 h and absence of reperfusion therapy. Clinical data included demographics, time metrics, stroke severity, pre-morbid, and 3-month Modified Rankin Score. Baseline computed tomography perfusion and MRI <72 h were performed. Volumes of Tmax utilizing different thresholds and FIVs were calculated. Spearman correlation was used to evaluate relationship involving ordinal variables and calculate the optimal Tmax threshold against FIV in both populations. RESULTS: Two hundred patients were included in the study sample, 100 in Jakarta and 100 in Geelong. The median National Institutes of Health Stroke Scale (IQR) were 6 (3-11) and 3 (1-5), respectively. The median Tmax+6 (IQR) was 0 (0-46.5) in Jakarta group and 0 (0-7.5) in Geelong group. The median FIV (IQR) was 0 (0-30.5) and 0 (0-5.5). Tmax+8 s in Jakarta population against FIV showed Spearman's coefficient ρ = 0.72, representing the optimal Tmax threshold. Tmax+6 s showed Spearman's coefficient ρ = 0.51 against FIV in the Geelong population. CONCLUSION: Tmax thresholds approximating FIV were possibly different in the Asian when compared with the non-Asian populations. Future studies are required to extend and confirm the validity of our findings.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".