Bibliographic record
Abstract
Since 2003, four articles on stroke biomarkers as potential diagnostic tests have been published in Clinical Chemistry (1)(2)(3)(4). Each has described an initial foray into attempting to find a holy grail in clinical stroke—a blood test for stroke—and each has provided early exciting results that suggest that we may yet welcome a new era of stroke diagnostics into the clinical realm. Human stroke is remarkably heterogeneous. Stroke varies widely in severity with a majority of strokes being mild. Stroke comes in 3 major types. In North America and Europe, ischemic stroke comprises 85% of all strokes, intracerebral hemorrhage 8%, and subarachnoid hemorrhage 7%. These ratios differ in Asia with up to 30% of strokes occurring as intracerebral hemorrhages. For the neurologist, stroke is relatively easy to diagnose clinically. It is the most common cause of a sudden acute neurologic deficit in both adults and children. Imaging is the mainstay of identifying stroke type because it is not possible to distinguish ischemia from hemorrhage reliably on clinical grounds alone.
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 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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.032 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".