A Lesson From 2020: Public Health Matters for Both COVID-19 and Diabetes
Bibliographic record
Abstract
Each January, the editors of Diabetes Care look back at the last year and forward to the next. In January 2021 we have much to be thankful and happy about. The journal continues to publish outstanding scientific reports together with illuminating and provocative Commentary, Perspective, and Review articles. We are indebted to the authors who submit their manuscripts, the reviewers who evaluate them, and the editorial and production group that manages the process. They make it all possible. In 2020, Diabetes Care ’s impact factor increased once again, from 15.27 to 16.02. Accumulating scientific evidence presented by our journal and others continues to improve understanding of the pathophysiology of diabetes and add to the array of treatments. And yet . . . it’s been a hell of a year in other ways. The coronavirus disease 2019 (COVID-19) pandemic started a year ago and still has the world in its grip. It has tested all of us and brought many activities nearly to a halt. Countless people have fallen ill, sadly many have died, and the daily routines of most families are disturbed. The lockdown to prevent spread of the virus keeps people at home, limits travel, harms businesses, closes schools, and interferes with diagnosis and treatment of other ailments. Acute medical facilities have been overwhelmed in some regions. Fierce debates about controlling the spread of COVID-19 and mitigating its human and economic costs have ensued. Yet, in this crisis we see much heroism. Medical personnel and those who support …
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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 | 0.037 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.038 | 0.079 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".