Experiência da trombectomia mecânica no tratamento do acidente vascular cerebral agudo em um hospital universitário brasileiro
Bibliographic record
Abstract
Background: Brazil is a developing country struggling to reduce its extreme social inequality, which is reflected on shortage of health-care infrastructure, mainly to the low-income class, which depends exclusively on the public health system.In Brazil, less than 1% of stroke patients have access to intravenous thrombolysis in a stroke unit, and constraints to the development of mechanical thrombectomy in the public health system increase the social burden of stroke.Objective: Report the feasibility of mechanical thrombectomy as part of routine stroke care in a Brazilian public university hospital.Patients and methods: Prospective data were collected from all patients treated for acute ischemic stroke with mechanical thrombectomy from June 2011 to March 2016.Combined thrombectomy was performed in eligible patients for intravenous thrombolysis if they presented occlusion of large artery.For those patients ineligible for intravenous thrombolysis, primary thrombectomy was performed as long as there was no evidence of significant ischemia for anterior circulation stroke (Alberta Stroke Program Early CT score >6) within a 6-hour time window, and also for those patients with wake-up stroke or posterior circulation stroke, regardless of the time of symptoms onset.Results: A total of 161 patients were evaluated, resulting in an overall successful recanalization rate of 76% and symptomatic intracranial hemorrhage rate of 6.8%.At 3 months, 36% of the patients had modified Rankin Scale score less than or equal to 2. The overall mortality rate was 23%.Conclusion: Our study, the first ever large series of mechanical thrombectomy in Brazil, demonstrates acceptable efficacy and safety results, even under restricted conditions outside the ideal scenario of trial studies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".