Perceived Barriers to the Adoption of Alternatives to Laboratory Animal Use for Rabies Diagnosis
Bibliographic record
Abstract
The use of laboratory animals is still common practice, but some uses can be replaced by alternative methods, such as Virus Isolation in Cell Culture (VICC) instead of the Mouse Inoculation Test (MIT) for rabies diagnosis. The objective of this work was to describe current rabies diagnosis methods in Brazil and other countries, and the constraints associated with replacing this use of mice with alternative methods. Nine out of 12 Brazilian and 14 out of 43 non-Brazilian respondents reported that they currently used the MIT. Respondents in countries other than Brazil, male respondents, and those already employing in vitro methods for rabies diagnosis, expressed higher levels of support for the use of alternatives. The most frequently reported constraints associated with the use of alternatives were lack of laboratory facilities, equipment and materials (cited 17 times by respondents), and lack of financial resources (cited 15 times). The results indicate that many laboratories continue to use mice for rabies diagnosis. The proportion of laboratories that use mice appears to be especially high in Brazil, despite animal protection laws and technical guidelines that favour the use of alternatives. The barriers to the adoption of alternative methods identified in the current study provide a basis for facilitating changes in Brazil and elsewhere.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".