Bibliographic record
Abstract
Recent advances in the treatment of acute ischaemic stroke have focused largely on drug treatments, and yet the number of effective and widely practicable treatments remains limited. After a spate of trials with negative results, no neuroprotective agents have yet been licensed for acute stroke. Although thrombolysis with tissue plasminogen activator is now available in the United States and Canada, most eligible patients are not treated, and thrombolysis remains the subject of considerable debate in the international research community. 1 2 Other important interventions for people with acute stroke include organised care in multidisciplinary stroke units and routine use of aspirin in acute ischaemic stroke. 3 4 Stroke is the second most common cause of death worldwide, and with no major panacea for acute stroke imminent, we must not ignore stroke prevention.5 Medical and surgical treatments to prevent stroke carry some risk (and some cost). These preventive strategies should be targeted at those who are at the highest absolute risk of stroke, because these individuals are likely to derive the greatest absolute benefit.6 These patients generally have a history of occlusive vascular diseases with symptoms— that is, prior ischaemic stroke or transient ischaemic attack, coronary heart disease, or peripheral vascular disease. Among the 80% of patients who survive an acute stroke, the risk of recurrent stroke is highest within the first few weeks and months; about 10% in the first year and about 5% per year thereafter. These patients are also at a major risk of other vascular disease, including myocardial infarction, emphasising the need for early preventive treatments.7 Individual risk factors such as a history of hypertension, smoking, hyperlipidaemia, increased blood glucose concentration, and obesity are important considerations for all patients, especially those at high risk. #### Summary points Reduction of blood pressure is effective at preventing a first …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".