Bibliographic record
Abstract
Background: The synchronous occurrence of acute ischemic stroke and acute myocardial infarction is an extremely rare condition that can be lethal.The causes and optimal treatment in these cases is still unclear. Methods:We conducted on the literature review, we analyzed clinical presentations, causes, type of myocardial infarction, site of stroke, modified ranking scale at discharge and treatment options.We compare mortality rate at hospital discharge and 90 days after discharge between patients with combination intervention treatment (both percutaneous coronary intervention for coronary arteries and mechanical thrombectomy for cerebral vessel) and medical treatment.Results: We identified 94 cases of concurrent cardio-cerebral infarction from case reports and case series.The mean age was 62.5±12.6 years.Male 58 patients (61.7%).In patients with combination intervention treatment group: hospital mortality rate was 13.3% and 90-days mortality rate was: 23.5% compared with mortality rate in medical treatment (23.5% at hospital and 59.5% at 90 days (P value 0.038 and 0.012 respectively) Conclusion: Concurrent cardio-cerebral infarction prognosis is very poor, without intervention about 25% of patients died before discharge 60% of patients died at 90 days after stroke.Despite only one quarter of patients treated by combination intervention treatment, this treatment modality significantly reduces mortality rate compared medical treatment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".