Physician Knowledge of the Benefits, Risks, and Contraindications of Tissue Plasminogen Activator for Acute Ischemic Stroke
Bibliographic record
Abstract
I read with great interest the detailed study of atrial fibrillation 1 in the context of acute stroke with particular relevance to the higher 3-month mortality of acute stroke patients who have atrial fibrillation (AF).We have previously reported 2 the higher 3-month mortality of AF patients presenting with a stroke.It should be recognized, however, that AF is only one aspect of the cardiovascular disease (CVD) of these patients and does not usually exist independently of other types of CVD (eg, ischemic heart disease and cardiac failure) in the older age group.It is vital that a more detailed assessment be performed for outcome measurement in patients with AF to take into account the presence of other CVDs.We have reported 2 that although AF was associated with a higher 3-month mortality in acute stroke patients (Pϭ0.05),there was no significant association of AF with acute phase mortality, ie, death in acute wards (Pϭ0.24).More significantly, we found that it is the presence of any degree of cardiac failure in addition to AF or other CVDs that is significantly associated with higher mortality both in the acute phase and at 3 months (PϽ0.001).Any future studies of the relationship of AF in the context of acute stroke should also study the influence of coexistent cardiac failure and other CVDs, eg, ischemic heart disease.In addition, it is also difficult to completely separate the independent influence of higher age on stroke mortality, because the patients with AF are much older than those without AF.Higher age is an independent factor that influences mortality after acute stroke. 3 It is similarly important that these factors be considered in intervention studies in acute 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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".