Case-control study of risk factors for high within-flock small-ruminant brucellosis prevalence in a brucellosis low-prevalence area
Bibliographic record
Abstract
A case-control study was conducted in a brucellosis low-prevalence area of NW Spain to determine factors associated with high within-flock small-ruminant brucellosis prevalence in 1998. Forty-one cases and 69 controls were selected and information from both official sources and personal interviews was retrieved for every flock. The relationship between variables obtained and flock status was assessed by unconditional multivariable logistic regression analysis. The introduction of replacement animals into the flock, the presence of older farmers, an inadequate brucellosis vaccination programme and higher flock seroprevalence in the town in 1997 were positively associated with case flocks. Thus, specific actions directed at farms presenting these characteristics should be included within official eradication programmes. In addition, for the 1999 campaign the time from sampling to culling the seropositive animals correlated positively (r=0.53; P<0.01) with the flock seroprevalence the following year, suggesting the need for a faster removal of the infected animals to increase the efficacy of the eradication campaigns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".