Update on<i>Actinobacillus pleuropneumoniae</i>-knowledge, gaps and challenges
Bibliographic record
Abstract
Porcine pleuropneumonia, caused by the bacterial porcine respiratory tract pathogen Actinobacillus pleuropneumoniae, leads to high economic losses in affected swine herds in most countries of the world. Pigs affected by peracute and acute disease suffer from severe respiratory distress with high lethality. The agent was first described in 1957 and, since then, knowledge about the pathogen itself, and its interactions with the host, has increased continuously. This is, in part, due to the fact that experimental infections can be studied in the natural host. However, the fact that most commercial pigs are colonized by this pathogen has hampered the applicability of knowledge gained under experimental conditions. In addition, several factors are involved in development of disease, and these have often been studied individually. In a DISCONTOOLS initiative, members from science, industry and clinics exchanged their expertise and empirical observations and identified the major gaps in knowledge. This review sums up published results and expert opinions, within the fields of pathogenesis, epidemiology, transmission, immune response to infection, as well as the main means of prevention, detection and control. The gaps that still remain to be filled are highlighted, and present as well as future challenges in the control of this disease are addressed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".