Evaluating interventions against<i>Salmonella</i>in broiler chickens: applying synthesis research in support of quantitative exposure assessment
Bibliographic record
Abstract
A scoping study and systematic review-meta-analyses (SR-MAs) were conducted to evaluate the effectiveness of various interventions for Salmonella in broiler chicken, from grow-out farm to secondary processing. The resulting information was used to inform a quantitative exposure assessment (QEA) comparing various control options within the context of broiler chicken production in Ontario, Canada. Multiple scenarios, including use of two separate on-farm interventions (CF3 competitive exclusion culture and a 2% lactose water additive), a package of processing interventions (a sodium hydroxide scald water disinfectant, a chlorinated post-evisceration spray, a trisodium phosphate pre-chill spray and chlorinated immersion chilling) a package consisting of these farm and processing interventions and a hypothetical scenario (reductions in between-flock prevalence and post-transport concentration), were simulated and compared to a baseline scenario. The package of on-farm and processing interventions was the most effective in achieving relative reductions (compared to baseline with no interventions) in the concentration and prevalence of Salmonella by the end of chilling ranging from 89·94% to 99·87% and 43·88% to 87·78%, respectively. Contaminated carcasses entering defeathering, reductions in concentration due to scalding and post-evisceration washing, and the potential for cross-contamination during chilling had the largest influence on the model outcomes under the current assumptions. Scoping study provided a transparent process for mapping out and selecting promising interventions, while SR-MA was useful for generating more precise and robust intervention effect estimates for QEA. Realization of the full potential of these methods was hampered by low methodological soundness and reporting of primary research in this area.
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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 | 0.168 | 0.293 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.025 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".