Can phytogenic additives improve the performance of broilers and replace growth-promoting antibiotics? A meta-analytic approach
Bibliographic record
Abstract
Phytogenic additives have been studied intensively in broiler chicken production to substitute growth-promoting antibiotics. However, the comprehensive literature on this topic makes it difficult to understand overall results because there are a noticeable number of studies with conflicting conclusions. While several research studies have shown that phytogenic additives may increment broiler chicken’s performance, others make the opposite evident. This study aimed to organize and understand information through meta-analysis considering a great number of publications and the factors that may interfere in the results of phytogenic additives, evaluating whether phytogenic additives can be used as a performance-enhancing additive for broilers, when compared with the effectiveness of growth-promoting antibiotics. The main factor that interferes in the evaluation of phytogenic additives is the microbiological challenge. Phytogenic additives improved average daily gain (ADG) (P < 0.001) and feed conversion (P < 0.001) regardless of microbiological challenge; however, they were worse compared with antibiotics under higher challenge (P < 0.020). A meta-regression of ADG in function of average daily feed intake confirmed that phytogenic additives increased the feed efficiency of broilers, but with less effectiveness than antibiotics. The blends of phytogenic additives increased the ADG in relation to the isolated use of only one phytogenic additive.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".