Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Individuals with diabetes are at a greater risk of hospitalization and mortality resulting from viral, bacterial, and fungal infections. The coronavirus disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread quickly to more than 213 countries and claimed 395,779 lives as of June 7, 2020. Notably, in several studies, diabetes is one of the most reported comorbidities in patients with severe COVID-19. SCOPE OF REVIEW: In this review, I summarize the clinical data on the risk for infectious diseases in individuals with diabetes while highlighting the mechanisms for altered immune regulation. The focus is on coronaviruses. Based on the new clinical data obtained from COVID-19 patients, a discussion of mechanisms, such as cytokine storm, pulmonary and endothelial dysfunction, and hypercoagulation, that may render individuals with diabetes more vulnerable to COVID-19 is provided. MAJOR CONCLUSIONS: Epidemiological studies show that poorly controlled diabetes is a risk factor for various infectious diseases. Given the global burden of diabetes and the pandemic nature of coronaviruses, understanding how diabetes affects COVID-19 severity is critical to designing tailored treatments and clinical management of individuals affected by diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it