Are adipokines related to COVID-19 and its severity? A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: The relationship between several adipokines and COVID-19 severity has lately been evaluated, results being inconclusive. Therefore, we aimed to assess the association between adipokines in COVID-19 and its severity. Methods: A search was performed in PubMed, Scopus, and Embase using predefined keywords. The Newcastle of Ottawa Scale (NOS) was used for the quality assessment of included studies. The main summary outcome was the mean difference (MD) in adipokine levels. Results: A total of 8 studies involving 473 individuals were included. A significant MD in serum adiponectin levels was demonstrated in mild vs. severe COVID-19 patients (-5.734 [95% CI -11.215 - -0.252]), with no significant MD in mild vs. moderate (-7.117 [95% CI -19.546 - 5.313]), or moderate vs. severe COVID-19 (-1.846 [95% CI -4.516 - 0.824]). Moreover, no significant MD was found in adiponectin and leptin levels when comparing COVID-19 patients vs. controls (-12.675 [95% CI -36.159 - 10.808]) and (8.034 [95% CI -10.403 - 26.471]), respectively. Conclusion: Adiponectin levels were significantly increased in patients with severe compared to mild COVID-19. However, no significant MD was found in adiponectin levels in mild vs. moderate and moderate vs. severe COVID-19 patients, nor in adiponectin and leptin levels in COVID-19 patients vs. controls.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".