Response to Thrombolysis in Patients with a Diagnosis of Cancer: A Post Hoc Analysis of the AcT Trial
Bibliographic record
Abstract
BACKGROUND: There is an increasing number of patients with cancer and acute ischemic stroke (AIS). We aim to compare outcomes in patients treated with thrombolysis for AIS with a history of cancer to those without. METHODS: This is a post hoc analysis of the Intravenous tenecteplase compared with alteplase for acute ischaemic stroke in Canada (AcT) trial, evaluating tenecteplase versus alteplase in patients with AIS within 4.5 h of onset. ICD-10 codes via administrative data linkage were used to identify a history of cancer. Primary outcome was modified Rankin Scale (mRS) 0-2 at 90 days. Other outcomes included mRS 0-1 at 90 days, return to pre-stroke function, mortality and bleeding. Analysis was done using logistic regression for binary outcomes adjusted for age, stroke severity, presence of cancer history and time from onset to needle. A generalized linear regression model was used for numeric outcomes, with effect measures reported as adjusted risk ratios (aRR). RESULTS: Of the 1577 patients enrolled, 37 (2.35%) had a prior diagnosis of cancer. At 90 days, cancer patients were less likely to achieve 90-day mRS 0-2 (aOR of 0.33 [95% CI 0.15-0.75]) and had higher mortality (aOR 3.75 [95% CI 1.76-7.75]) as compared to those without cancer. Length of stay was longer in patients with cancer than those without cancer (median 11.5 days [IQR 7-24.5] vs 5 days [IQR 3-11], respectively, aRR 2.76 [95% CI 2.58-2.94]). CONCLUSION: Patients with AIS and a history of cancer had worse functional outcomes, prolonged length of stay and higher rates of mortality as compared to those with no diagnosis of cancer.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".