Cerebral Infarction Caused by Trousseau’s Syndrome Associated With Lung Cancer
Bibliographic record
Abstract
Background: Lung cancer is one of the common cancers that can cause Trousseau's syndrome. However, there are few reports of cerebral infarction due to Trousseau's syndrome associated with lung cancer. The aim of this study is to investigate the clinical features of lung cancer-related cerebral infarction and effective management practice. Methods: Japanese patients diagnosed with Trousseau's syndrome-related cerebral infarction associated with lung cancer between August 2012 and November 2021 in our hospital were retrospectively enrolled. Clinical data, treatment, and outcomes of the patients were collected. Results: Ten patients were enrolled. The median age was 65 years (range: 43 - 84 years). All patients had advanced lung cancer. The histological types were adenocarcinoma (n = 8), pleomorphic carcinoma (n = 1), and small cell lung cancer (n = 1). Recurrent cerebral infarction occurred in six patients. Among four patients who had continued heparin since the initial infarction, recurrence occurred in one. D-dimer was high in all 10 patients at the initial cerebral infarction. D-dimer level at the time of recurrent cerebral infarctions was higher than that at the first cerebral infarctions. Since performance status declined in nine patients, one patient continued anticancer drugs after cerebral infarction. Four patients died within 100 days of the onset of cerebral infarction. Conclusions: Cerebral infarction of lung cancer-related Trousseau's syndrome has poor prognosis. Heparin may be effective in controlling the condition. In addition, D-dimer may serve as a marker of cancer-related thrombosis.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".