Bibliographic record
Abstract
Diabetes is highly linked to the severity of coronavirus disease 2019 (COVID-19). My recent meta-analysis also suggested a higher prevalence of diabetes in severe COVID-19 as compared with non-severe COVID-19. Recent observational studies have shown that hyperglycemia was significantly associated with severity of COVID-19 in both diabetic and non-diabetic patients. To prevent worse outcome of COVID-19, more tight glucose control is required. I studied the association between hyperglycemia and worse outcome of COVID-19, the putative beneficial and harmful effects, and clinical outcomes of oral hypoglycemic drugs and insulin use in glycemic control among COVID-19 patients, by searching literatures. Although there were some negative studies, the meta-analysis reported that the treatment using metformin was associated with reduction in mortality due to COVID-19. One study showed that treatment with sitagliptin, one of dipeptidyl peptidase-4 (DPP4) inhibitors, during hospitalization was associated with reduction of mortality, with a clinical improvement as compared with patients on the standard care. There were no clinical studies showed effects of glucagon-like peptide-1 receptor agonists, pioglitazone and sulfonylurea on COVID-19 outcomes. Regarding sodium-glucose cotransporter 2 (SGLT2) inhibitors, a case of euglycemic diabetic ketoacidosis (DKA) associated with COVID-19 and a case of DKA that was difficult to distinguish from COVID-19 were reported. COVID-19 patients who need hospital care may deteriorate rapidly, an early and appropriate initiation of insulin therapy in hyperglycemic COVID-19 patients may be to be encouraged. J Endocrinol Metab. 2021;11(1):1-7 doi: https://doi.org/10.14740/jem718
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".