Third International Stroke Trial
Bibliographic record
Abstract
Stroke is a common disease. Every year, stroke kills about 5 5 million people around the world (1). Almost 5 billion years of healthy life are lost as a result of yearly strokes (1). In the United Kingdom, it is the single most expensive condition looked after by the National Health Service, costing 2 8 billion pounds (5 2 billion US dollars) in direct care costs alone (2). In Europe, Australia and the United States, about 80% of strokes are ischaemic (3). Unfortunately, ischaemic stroke has few effective treatments. Coordinated stroke unit care reduces the number of patients left dead or dependent due to stroke by 56 per 1000 patients treated (3). Once established, stroke units can care for most patients with stroke, so that their effect on improving outcome is larger than any other treatment. Treatment with aspirin soon after ischaemic stroke reduces the chance of death or disability by about 12 patients per 1000 treated, probably by reducing the chance of stroke recurrence (4). Although the treatment effect is small, aspirin is cheap and widely applicable so it too has a large effect on reducing the overall burden of disability from stroke. Thrombolysis for stroke with recombinant tissue plasminogen activator (rtPA) is effective, though expensive and difficult to deliver, and currently applicable to only a small group of patients. Consequently, the impact of thrombolysis on reducing stroke-related disability in the population from stroke is currently relatively small (5). Only stroke units and aspirin are in widespread use for the treatment of acute ischaemic stroke.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.129 | 0.037 |
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".