P.064 Clinical correlates of pre-morbid cancer in a consecutive sample of individuals with ischemic stroke
Bibliographic record
Abstract
Background: Ischemic stroke (IS) may be the first sign of an occult cancer, due to an underlying paraneoplastic prothrombotic state. Predictors of occult cancer in acute IS, however, remain unclear. We performed a single-center study to identify clinical features that may distinguish cancer-associated IS from IS without recent cancer. Methods: We reviewed consecutive admissions for acute IS at our institution between January and December 2020. Recent cancer was defined as any new diagnosis of cancer up to five years prior to IS. We compared clinical features with Fisher and chi-squared tests for categorical data, as well as t-tests and Mann-Whitney U tests for continuous data. Results: We included 169 patients in the non-cancer group and 19 in the recent cancer group (median time for cancer diagnosis: 10.5 months). The most frequent primary site was the digestive system (n=5; 33.3%). Patients with recent cancer had a significantly lower mean BMI (19.3 vs 26.4 kg/m2; p=0.013), lower mean hemoglobin (123 vs 134 g/L; p=0.015), and more frequent prior venous thrombosis (15.8% vs 1.2%; p=0.008) than cancer-free patients. Conclusions: Clinical features such as lower BMI, lower hemoglobin and prior venous thrombosis may help identify cancer-associated mechanisms, as well as guide cancer screening, in IS.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".