Unraveling the relationship between ACTH and cortisol levels in COVID-19 infections: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: In December 2019, a novel pneumonia associated with the 2019 coronavirus emerged unexpectedly. However, limited data exist on the effects of COVID-19 on ACTH and cortisol levels. To address this gap in knowledge, we conducted a meta-analysis of published studies on the relationship between COVID-19 patients and their ACTH and cortisol levels. METHODS: We conducted a thorough search of the PubMed, Embase, Cochrane Library, and Web of Science databases up until May 2023. We assessed the relevance of each study we found, specifically looking for studies that reported on ACTH and cortisol levels in COVID-19 patients. We calculated weighted mean differences (WMD) and 95% confidence intervals (CI) to investigate the relationship between ACTH and cortisol levels in COVID-19 patients. We evaluated the quality of each study using the Newcastle Ottawa scale (NOS), and we assessed publication bias using Begg's rank correlation test, Egger's test, and funnel plot. We conducted our meta-analysis using the Stata 12.0 (Stata Corporation, TX). RESULTS: Our search yielded nine studies that met our inclusion criteria, which included a total of 440 COVID-19 patients and 474 controls, with data up to May 2023. Seven of these studies reported on ACTH levels, and six studies reported on cortisol levels. Our findings revealed that COVID-19 patients had significantly higher levels of cortisol compared to controls (WMD 3.46 (95% CI 2.29 to 4.62)). However, there was no significant difference in ACTH levels between COVID-19 patients and controls (WMD 1.58 (95% CI -5.79 to 8.94)). CONCLUSIONS: This meta-analysis indicates a potential relationship between elevated cortisol levels and COVID-19 infection. However, more well-designed, adequately powered, randomized controlled trial will be needed to assess the use of cortisol in patients with COVID-19 infection.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".