Coenzyme Q10 Supplementation on Tilapia Growth Performance: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
An increasing body of evidence suggests that Coenzyme Q10 can enhance fish performance, which is vital for the development of aquaculture; however, its effects have not yet been systematically analysed. This paper aims to summarise and evaluate the effects of Coenzyme Q10 on the growth performance of Tilapia. Five databases were searched: PubMed, ScienceDirect, Scopus, Web of Science, and Google Scholar. The study followed the PRISMA methodology and was registered on the Open Science Framework. Methodological quality was assessed using the SYRCLE Risk of Bias Tool. Statistical analyses were conducted using RevMan software. Relevant literature was screened, and data was extracted. In total, seven studies were included in the review, with five included in the meta-analysis, comprising 1,752 animals and 18 comparisons. Most studies exhibited methodological limitations or uncertainties. The meta-analysis demonstrated that Coenzyme Q10 significantly improved performance indicators, with standardised mean differences (SMD) observed for feed intake (SMD = 10.49, 95% CI: 8.58; 12.40), feed conversion (SMD = -2.03, 95% CI: -3.00; -1.07), feed efficiency ratio (SMD = 8.83, 95% CI: 7.19; 10.48), daily weight gain (SMD = 11.28, 95% CI: 9.42; 13.14), specific growth rate (SMD = 7.17, 95% CI: 5.68; 8.67), and survival rate (SMD = 2.80, 95% CI: 2.03; 3.57), compared with the control. Subgroup analyses indicated significant effects of Coenzyme Q10 on most variables. Although CoQ10 may improve growth performance indices in tilapia, further research is needed to strengthen species-specific evidence.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".