Efficacy of thymosin-α-1 in patients with COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To identify whether thymosin-α-1 (Tα1) is effective in patients with Coronavirus disease 2019 (COVID-19) and to determine a suitable population for Tα1 treatment. Methods We included studies with ≥10 cases and adults (aged ≥18 years) with laboratory-confirmed SARS-CoV-2 infection, data on mortality or length of hospitalization, disease severity, and study location, while excluded pregnant and breastfeeding women and minors. Publications were searched from November 1, 2019, to July 5, 2023, in six databases, including PubMed, Web of Science, Embase, Cochrane Library, China Knowledge Resource Integrated Database, and Wanfang Database. We separately utilized Newcastle-Ottawa Scale and Cochrane handbook methodology to evaluate risk of bias and used Review Manager (version 5.4, Cochrane Collaboration, Copenhagen, Denmark) to present and synthesize results. Relative risks (RR) and Standardized Mean Difference (SMD) with 95% confidence intervals (CI) were analyzed for dichotomous variables and continuous variables, respectively. Results Nine studies (participants = 5417) were included. No significant differences were found in mortality (nine studies; n = 5417; RR = 0.95; 95% CI: 0.56, −1.60; p = .84; I 2 = 90%) or length of hospitalization (four studies; n = 3688; SMD = 0.16; 95% CI: −0.38, −0.69; p = .57; I 2 = 96%) between patients with COVID-19 who did and did not receive Tα1. Participants were divided by the severity of the disease (serious and non-serious) and study location. Among the serious group, the incidence of death among patients who received Tα1 treatment was 0.67 times that of patients who did not receive Tα1 treatment (four studies; n = 1230; RR: 0.67; 95% CI: 0.58, −0.77; p < .00,001; I 2 = 0%). There was no significant difference in length of hospitalization between the groups (two studies; n = 410; SMD = 0.66; 95% CI: −0.06, −1.38; p = .07; I 2 = 87%). Among the non-serious group, compared to not having Tα1 treatment, receiving Tα1 treatment reduced hospitalization length (two studies; n = 3670; SMD = −0.28; 95% CI: −0.41, −0.14; p < .0001; I 2 = 51%), while no significant difference in mortality (three studies; n = 3775; RR = 1.06; 95% CI: 0.22, −5.03; p = .94; I 2 = 89%). Moreover, there was no significant difference between subgroups when divided by study locations (Studies within China: seven studies; n = 5263; RR = 1.14; 95% CI: 0.64, −2.04; p = .65; I 2 =92%; Studies outside of China: two studies; n = 154; RR = 0.41; 95% CI: 0.14, −1.24; p = .11; I 2 = 51%). Discussion For patients with serious types of COVID-19, Tα1 significantly decreased mortality, which supports the utilization of Tα1 in patients with severe and critical types of COVID-19. Moreover, regarding hospitalization length, patients with non-serious COVID-19 who used Tα1 reduced their hospitalization length compared to those that did not use Tα1. However, these results have high heterogeneity and limited generalizability.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".