Bibliographic record
Abstract
Background: We hypothesized that abnormal blood platelet count (BPC) is associated with poorer outcomes after acute ischemic stroke. Methods: We included data from the Registry of the Canadian Stroke Network on consecutive patients with acute ischemic stroke admitted between July/2003 and March/2008. Patients were divided into groups as follows: low BPC (<150,000/mm3), normal BPC (150,000 to 450,000/mm3) and high BPC (>450,000/mm3). Primary outcome measures were the frequency of moderate/severe strokes on admission (Canadian Neurological Scale: <8), greater degree of disability at discharge (modified Rankin score: 3-6), and 30-day and 90-day mortality. Results: We included 9,230 patients. Both low and high BPC were associated with higher 30-day mortality (p=0.0103) and 90-day mortality (p=0.0189) following acute ischemic stroke. The Kaplan-Meier curves indicate that abnormal BPC is associated with greater mortality after acute ischemic stroke (p=0.0002). Nonetheless, abnormal BPC was not associated with degree of impairment (p=0.3734), degree of disability (p=0.684), or length of stay (LOS) in the acute stroke care center (p=0.9541) after adjustment for major potential confounders. Conclusions: In patients with acute ischemic stroke, thrombocytopenia and thrombocytosis on the initial admission is associated with higher mortality after stroke. Abnormal BPC does not adversely affect the degree of impairment and disability, or LOS in the acute care center after acute ischemic stroke.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.306 | 0.148 |
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".