Treatment of acute ischemic stroke in patients with cerebral microbleeds: a decision analysis
Bibliographic record
Abstract
Treatment with anti-thrombotic agents reduces the likelihood of recurrent ischemic stroke. However, it may produce intracerebral hemorrhage. The risk of recurrence is highest shortly after an ischemic stroke has occurred irrespective of the ischemic stroke subtype. About one-fourth1 to one-half2 of the risk of recurrence at 1 year is accrued during the acute phase (first 2–4 weeks) of an ischemic stroke, and this risk declines further during the following 5 years.3 Similarly, the rates of intracerebral hemorrhage decline from 0.6% to 3% during the the first 2–4 weeks after an ischemic stroke4–13 to 0.16–0.23% annually thereafter.11,14 This time variance in complication rates indicates that the risk–benefit ratio of the treatment of patients with acute ischemic stroke may be different from that of their long-term care. Still, published analyses of the treatment of such patients have assumed that the risks of ischemic stroke recurrence and intracerebral hemorrhage are constant over the patient’s lifetime,15 or over the first year after ischemic stroke.16 Therefore, the conclusions of these analyses may not pertain to patients with acute ischemic stroke. The management of ischemic stroke in patients with cerebral microbleeds is particularly uncertain. About one-fourth of the patients with ischemic stroke have cerebral microbleeds that may predict a higher risk of intracerebral hemorrhages.17 Still, we know of no randomized studies comparing the risks and benefits of anti-thrombotic treatment in patients with and without cerebral microbleeds.
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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".