Systematic review of the magnitude of change in prevalence and quantity of <i>Salmonella</i> after administration of pathogen reduction treatments on pork carcasses
Bibliographic record
Abstract
OBJECTIVE: In this systematic review, we summarized change in Salmonella prevalence and/or quantity associated with pathogen reduction treatments (washes, sprays, steam) on pork carcasses or skin-on carcass parts in comparative designs (natural or artificial contamination). METHODS: In January 2015, CAB Abstracts (1910-2015), SCI and CPCI-Science (1900-2015), Medline® and Medline® In-Process (1946-2015) (OVIDSP), Science.gov, and Safe Pork (1996-2012) were searched with no language or publication type restrictions. Reference lists of 24 review articles were checked. Two independent reviewers screened 4001 titles/abstracts and assessed 122 full-text articles for eligibility. Only English-language records were extracted. RESULTS: Fourteen studies (5 in commercial abattoirs) were extracted and risk of bias was assessed by two reviewers independently. Risk of bias due to systematic error was moderate; a major source of bias was the potential differential recovery of Salmonella from treated carcasses due to knowledge of the intervention. The most consistently observed association was a positive effect of acid washes on categorical measures of Salmonella; however, this was based on individual results, not a summary effect measure. CONCLUSION: There was no strong evidence that any one intervention protocol (acid temperature, acid concentration, water temperature) was clearly superior to others for Salmonella control.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".