Bibliographic record
Abstract
Sir, Cancer is an important stroke etiology, and occult cancer may be often missed as stroke mechanisms in such cases are stamped as cryptogenic. In a group of patients with Embolic stroke of undetermined source (ESUS), 20% of patients had active cancer, and these patients had D-dimer levels twenty times higher compared to those without cancer.[1] The infarcts in these cases were in multiple vascular territories.[1] In fact, the “Three Territory Sign” is very specific for malignancy-related stroke and six times commoner than atrial fibrillation-related stroke.[2] One out of ten patients hospitalized with ischemic stroke in the United States had comorbid cancer.[3] If we detect cancer within a year after stroke, we may assume it to be present during the vascular event and might have a contribution towards it.[4] In a Canadian study involving 30,097 patients (age group 45–85 years), the diagnosis of cancer within the first year of a stroke was 2.4 times compared to individuals without stroke.[5] Patients aged between 15 and 49 years have up to fivefold increased chance of having cancer within first year of stroke diagnosis and incidence falls gradually with time (in a study from the Netherlands involving 390,398 patients).[6] In fact, arterial thromboembolism is commonly detected five months before cancer is diagnosed; with maximum incidence, a month before the detection of cancer.[7] Cancer patients have high chance of embolic stroke (detected by Transcranial Doppler) related to hypercoagulopathy, especially among those who do not have conventional stroke mechanism (CSM) and stamped as cryptogenic strokes compared to patients with CSM (57.9% vs 33.3%).[8] Interestingly, hypercoagulability is a double-eyed sword as it can also precipitate tumor growth and needs to be detected and treated at the earliest opportunity. Besides cancer-induced coagulopathy, cancer may speed up the CSMs. Mucin secreted into the bloodstream from adenocarcinoma provokes a coagulation cascade. Local infiltration of blood vessels occurs by tumor emboli or nonbacterial thrombotic endocarditis. Hyperleukocytosis in leukemia and hyperviscosity because of increased protein formation in multiple myeloma are other mechanisms. One out of four cancer patients are detected to have patent foramen ovale (PFO), while one out of five cancer patients have venous thromboembolism; thus, PFO and cancer are another important association.[6] Extracellular vesicles derived from cancer cells and NETosis markers were found prominently high in stroke patients with cancer.[1] Stroke among the young is on rise in every corner of the world and almost 50% of them are still cryptogenic. So, we need to search for markers of cancer-related stroke. Very high levels of D-Dimer, C-reactive protein (CRP), fibrinogen, cancer cell-derived extracellular vesicles, NETosis markers, micro-emboli (detection via Transcranial doppler [TCD]), and finally the presence of PFO in patients of cryptogenic stroke are probably such markers. Thus, when a patient comes with stroke (especially young), cancer needs to be ruled out even in the presence of conventional risk factors when the stroke mechanism remains undetermined. It will cause early detection of cancer and save precious time as we can intervene early. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".