Prevalent Infectious Causes of Abortion in the Ruminant Population in Iran- A Literature Review.
Bibliographic record
Abstract
Abortion is one of the most crucial problems for ranchers in Iran, in terms of economics, animal healthcare, and zoonotic diseases. The farm animal industry in Iran suffers major economic losses due to abortion each year. Until now, some epizootological studies have been conducted on infectious agents of ruminant abortion in Iran. However, there is no comprehensive information on the ruminant abortion status in Iran. Our goal was to collect all the available information on common infectious causes of abortion in ruminants in Iran to better understand the situation in the country. This review covers all published documents in the main English and Persian-language databases on infectious agents causing abortion inruminants (cattle, sheep, goats, camels and buffalo) in Iran from 1980 to May, 2024.Although ruminant abortion in Iran has multifactorial etiologies, the present study identified infectious diseases as a significant risk factor for abortion in ruminants . Important putative infectious agents that cause abortion in sheep and goats include toxoplasmosis, chlamydiosis, brucellosis and coxiellosis, while in cattle, the important infectious agents include neosporosis, BVDV and BoHV-1. According to our results, a well-defined control strategy for preventing and controlling infectious abortion in Iran should be based on further epidemiological studies on the cause of abortion, accurate recordkeeping, laboratory analysis, control of animal trafficking from neighboring countries and from one region to another within the country, the employment of good biosecurity practices that inhibit the introduction and spread of infectious causes of abortion, and the use of vaccination programs.
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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 | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".