Bibliographic record
Abstract
S troke in Russian joins Stroke en Español in addressingmajor language audiences other than English. The newversion of Stroke premieres this month in Russia. Russia has one of the world’s1 highest stroke incidence rates. Unlike neighboring Western Europe, where rates are falling,2 in Eastern Europe they remain high.3 Many causes account for this, such as increasing prevalence of risk factors and decreasing availability of medical care, both exacerbated by the turmoil following the disintegration of the Soviet Union. One step toward addressing the rising epidemic is the diffusion of up-to-date knowledge about all aspects of stroke. Professor Veronika I. Skvortsova, Editor Designate of Stroke in Russian, with the strong support of Professor Eugene I. Gusev, has put together an impressive Editorial Board from Russia and neighboring countries as well as Russian-speaking members of the Editorial Board from Canada and the United States. This Editorial Board will select published articles from Stroke and write commentaries to put them in context for the realities of Russia and other Eastern European countries. We welcome Professor Skvortsova as Editor and wish her, our Eastern European colleagues, and their audience, great success.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.354 | 0.273 |
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".