P.061 How neurologists screen for occult cancer in acute ischemic stroke
Bibliographic record
Abstract
Background: People with acute ischemic stroke (IS) have a higher prevalence of occult malignancy. Consensus is lacking, however, on the extent of cancer screening tests that should be offered in this population. We performed a single-center study to review curent cancer screening practices in acute IS. Methods: We reviewed consecutive admissions for acute IS at our institution between January and December 2020. We defined extensive cancer screening as i) a cancer investigation test falling outside Canadian guidelines, or ii) any chest, abdomen or pelvis imaging by CT, TEP/CT or ultrasound. We compared clinical features of people with and without extensive screening with Fisher and Mann-Whitney U tests. Results: Among 171 patients with acute IS, 11 (6.4%) underwent extensive cancer screening. A lower BMI was the only clinical feature associated with extensive cancer screening (p=0.013). Markers that were not associated with extensive screening included age (p=0.479), male sex (p=0.758), cryptogenic etiology (p=1.000), infarctions in multiple vascular territories (p=0.748), hemoglobin (p=0.505), fibrinogen (p=0.162) and C-reactive protein (p=0.442). Conclusions: Common predictors of occult cancer were not associated with more extensive cancer screening in this small sample of IS. Validated clinical prediction models may help clinicians guide cancer investigations 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".