Program for the Epidemiological Evaluation of Stroke in Tandil, Argentina (PREVISTA) Study: Rationale and Design
Bibliographic record
Abstract
The availability of population-based epidemiological data on the incident risk of stroke is very scarce in Argentina and other Latin American countries. In response to the priorities established by the World Health Organization and the United Nations, PREVISTA was envisaged as a population-based program to determine the risk of first-ever and recurrent stroke and transient ischemic attack incidence and mortality in Tandil, Buenos Aires, Argentina. The study will be conducted according to Standardized Tools for Stroke Surveillance (STEPS Stroke) methodology and will enroll all new (incident) and recurrent consecutive cases of stroke and transient ischemic attack in the City of Tandil between May 1st, 2013 and April 30, 2015. The study will include patients with ischemic stroke, non-traumatic primary intracerebral hemorrhage, subarachnoid hemorrhage, and transient ischemic attack. To ensure the inclusion of every cerebrovascular event during an observation period of two years, we will instrument an 'intensive screening program', consisting of a comprehensive daily tracking of every potential event of stroke or transient ischemic attack using multiple overlapping sources. Mortality would be determined during follow-up for every enrolled patient. Also, fatal community events would be screened daily through revision of death certificates at funeral homes and local offices of vital statistics. All causes of death will be adjudicated by an ad-hoc committee. The close population of Tandil is representative of a large proportion of Latin-American countries with low- and middle-income economies. The findings and conclusions of PREVISTA may provide data that could support future health policy decision-making in the region.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".