RELSA Severity Assessment & Humane Endpoint Forecasting
Combines body weight, temperature, clinical scores and biomarkers into a single RELSA severity score, then forecasts humane endpoints with ARIMA.
Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026
What it does
- RELSA scoring: merges multiple welfare readouts (weight loss, temperature, clinical scores, inflammatory biomarkers, activity, heart rate, burrowing) into one relative severity score per animal per time point (0 = baseline, 1 = reference set maximum).
- Humane endpoint forecasting (foRcast): fits an ARIMA model to an individual animal's RELSA trajectory, predicts the next score with a 95% prediction interval, and evaluates it with RMSE, PICP and MPIW.
- Severity zones: uses kernel density estimation to locate density minima as candidate attention/danger thresholds, with a required bandwidth sensitivity sweep.
- Reporting discipline: documents the four decisions that drive results (directionality, baseline normalization, reference set, zero-baseline score mapping), nine common pitfalls, explicit regulatory boundaries and a reproducibility checklist.
Who it's for
- Researchers, veterinarians and animal facility staff performing severity assessment.
- Anyone writing the severity section of a 3Rs/refinement analysis or an EU Directive 2010/63/EU application.
- Teams wanting to compare multivariate welfare data (telemetry, clinical scoring) on one common scale.
Examples
- Score a cohort: run
relsa_score.pyonassets/example_cohort.csv(6 mice, 9 days) with weight, temperature, a 0–8 clinical score and IL-6, select the endpoint group as reference, save it to JSON and export per-day RELSA plus per-variable weights. - Flag at-risk animals early: train up to the time point before euthanasia and predict the endpoint score (e.g. 0.93 [0.67–1.19] vs actual 1.00); for live monitoring use
--mode rollingone-step-ahead forecasts. - Define zones: run
kde_thresholds.pyto find a threshold at 0.703 and report it together with a bandwidth sweep, since a 10% larger bandwidth can remove the minima entirely.
· · · Install guide · · ·
Install in the Claude app (no terminal)
- Download the ZIP with the button below.
- In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
- Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Install in Claude Code
Let Claude do it — paste this into Claude Code
Install the skill I found on Claude Skill Mart. Copy the skills/relsa-severity-assessment folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/relsa-severity-assessment/. When it's done, tell me in one line what this skill can do.
Install with a command instead
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/relsa-severity-assessment ~/.claude/skills/⚠ This is a third-party skill. Check the source repository before installing.
- Open a terminal and change into your working directory.
- Clone the repository:
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git - Copy the skill into Claude's skills folder:
mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/relsa-severity-assessment ~/.claude/skills/ - Install the Python dependencies (Python >= 3.10):
uv pip install "numpy>=1.26" "pandas>=2.0" "scipy>=1.11" "statsmodels>=0.14" matplotlib - Restart Claude Code and ask something like "compute RELSA severity scores for my mouse cohort" to trigger the skill.
- Run the documented example commands against
assets/example_cohort.csvfirst to verify your setup, then switch to your own CSV.
View source on GitHub ↗License: MIT