First-Ever Stroke and Transient Ischemic Attack Incidence and 30-Day Case-Fatality Rates in a Population-Based Study in Argentina
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Epidemiological data about stroke are scarce in low- and middle-income Latin-American countries. We investigated annual incidence of first-ever stroke and transient ischemic attack (TIA) and 30-day case-fatality rates in a population-based setting in Tandil, Argentina. METHODS: We prospectively identified all first-ever stroke and TIA cases from overlapping sources between January 5, 2013, and April 30, 2015, in Tandil, Argentina. We calculated crude and standardized incidence rates. We estimated 30-day case-fatality rates. RESULTS: We identified 334 first-ever strokes and 108 TIAs. Age-standardized incidence rate per 100 000 for Segi's World population was 76.5 (95% confidence interval [CI], 67.8-85.9) for first-ever stroke and 25.1 (95% CI, 20.2-30.7) for first-ever TIA, 56.1 (95% CI, 48.8-64.2) for ischemic stroke, 13.5 (95% CI, 9.9-17.9) for intracerebral hemorrhage, and 4.9 (95% CI, 2.7-8.1) for subarachnoid hemorrhage. Stroke incidence was slightly higher for men (87.8; 95% CI, 74.6-102.6) than for women (73.2; 95% CI, 61.7-86.1) when standardized for the Argentinean population. Thirty-day case-fatality rate was 14.7% (95% CI, 10.8-19.5) for ischemic stroke, 24.1% (95% CI, 14.2-36.6) for intracerebral hemorrhage, and 1.9% (95% CI, 0.4-5.8) for TIA. CONCLUSIONS: This study provides the first prospective population-based stroke and TIA incidence and case-fatality estimate in Argentina. First-ever stroke incidence was lower than that reported in previous Latin-American studies, but first-ever TIA incidence was higher. Thirty-day case-fatality rates were similar to those of other population-based Latin-American 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".