Author Correction: Burdens of type 2 diabetes and cardiovascular disease attributable to sugar-sweetened beverages in 184 countries
Bibliographic record
Abstract
In the version of the article initially published, in the eighth paragraph of the Discussion, the text “Among large nations, the largest increases in SSB-related T2D burdens were in Mexico, Thailand and the United Kingdom, and in CVD burdens, Colombia, Nigeria, Thailand and Russia. These changes align with rises in SSB consumption in these nations 12 . Similarly, declining SSB-related cardiometabolic burdens in Brazil, the United States and the United Kingdom (for CVD) are consistent with their decreasing SSB consumption from 1990 to 2020 12 ” was incorrect and has now been updated to “Among largely populated nations, the largest increases in SSB-related T2D incidence was in Colombia, USA and Argentina; and in CVD incidence, Nigeria, Russia, Colombia and Thailand. These changes generally align with rises in SSB consumption in these nations, except in the US where slight declines in SSB consumption were offset by increased burdens of diabetes 12 . Similarly, declining SSB-related cardiometabolic burdens in Turkey, Brazil, and the United States and the United Kingdom for CVD are consistent with their decreasing SSB consumption from 1990 to 2020 12 .” Additionally, Supplementary Data 1 and 2 have been updated to remove decimals in values greater than 100. These corrections have been made to the HTML and PDF versions of the article.
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.027 |
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".