Humane Endpoints for Infectious Disease Animal Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The obligation to minimize or eliminate unnecessary pain and distress may experience when they are used in biomedical research, teaching, and testing is clearly stated in the Canadian Council on Animal Care guidelines on choosing an appropriate endpoint in experiments using for research, teaching, and testing: In experiments involving animals, any actual or potential pain, distress, or discomfort should be minimized or alleviated by choosing the earliest endpoint that is compatible with the scientific objectives of the research. Selection of this endpoint should involve consultation with the laboratory animal veterinarian and the animal care committee (CCAC 1998, p. 2). Additional guidance is provided for used in infectious disease studies, as follows: For all infectious disease research, including virulence tests in animal models, endpoints should be established that minimize the potential for pain and/or distress in the animals (CCAC 1998, p. 15). Since the work of pioneers such as Jenner and Pasteur (Fenner et al. 1997) more than a century ago, animal models have provided essential information in the study of infectious diseases. Although much experimentation can now be accomplished with in vitro systems, continue to be necessary for this field for studying the process of inflammation, specific infectious diseases, and pharmacologic treatment of infectious diseases; vaccine development and efficacy and safety testing; virulence testing; and, for diagnostic purposes, in both human and veterinary medicine. The use of whole animal models, both natural and induced (including transgenic models), is considered important to the understanding of the very complex temporal relationships that occur in infectious disease involving the body, its neuroendocrine and immune systems, and the infectious organism.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it