Benefits of non-invasive methods compared to telemetry for distress analysis in a murine model of pancreatic cancer
Bibliographic record
Abstract
Prospective severity assessment is legally required in many countries to ensure high-quality research along with high welfare standards for laboratory animals. Mice and rats, the most common laboratory species, are prey animals that usually suppress signs of pain and suffering. Therefore, highly sensitive readout parameters are necessary to adequately quantify distress. The present study compared the performance of different non-invasive methods in determining animal distress, such as measuring body weight, distress score, faecal corticosterone metabolites, burrowing, and nesting behaviour, with continuous monitoring of heart rate, body temperature and activity by telemetry. The distress caused by two surgical interventions was compared and the burden caused by tumour growth was described. Transmitter implantation caused higher distress than laparotomy plus carcinoma cell injection into the pancreas. Surprisingly, no significant increase in distress was observed during tumour growth. The receiver operating characteristic curve analysis revealed that some non-invasive distress-parameters, i.e., distress-score and burrowing activity, exhibited slightly better performance to quantify distress than the most suitable parameters measured by telemetry. Due to the high burden caused by the implantation of the telemetric device, the use of non-invasive methods to assess distress in laboratory animals after surgical interventions should be favoured in future studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".