relsa-severity-assessment

name	relsa-severity-assessment
description	Multivariate severity assessment and humane endpoint prediction for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint or when one will be reached, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.
license	MIT
allowed-tools	Read Write Edit Bash
compatibility	Requires Python >=3.10 with numpy, pandas, and scipy; statsmodels >=0.14 for forecasting and matplotlib for figures. Tested with numpy 2.5, pandas 3.0, scipy 1.18, statsmodels 0.14.6. No network access needed.
metadata	{"version":"1.0","skill-author":"K-Dense Inc."}

RELSA severity assessment and humane endpoint forecasting

Overview

Severity assessment in animal research is legally mandatory and scientifically load-bearing: it drives humane endpoint decisions, and poor welfare monitoring degrades reproducibility. The usual practice evaluates each readout in isolation — weight loss here, a clinical score there — which makes it hard to say how badly an individual animal is actually doing.

This skill implements two published procedures that address that:

• RELSA (Talbot et al., 2022) combines several outcome measures into one score per animal per time point, expressed relative to a reference set of known burden. RELSA = 0 is baseline; RELSA = 1 means the animal has reached the reference set's maximum deviation.
• foRcast (Lutscher et al., 2026) fits an ARIMA model to an individual animal's RELSA trajectory and forecasts the next score with a 95% prediction interval, so animals heading for a humane endpoint can be identified before they get there. Kernel density estimation on the RELSA scale supplies candidate attention and danger zones for interpretation.

The point is refinement: give at-risk animals attention earlier, and avoid euthanising animals that would have recovered. Both procedures are aids to severity assessment, not decision rules — see Boundaries.

When to use this skill

• Combining weight loss, temperature, clinical scoring, biomarkers, or telemetry into a single per-animal severity score
• Asking which animals in a cohort are at risk of reaching a humane endpoint, or predicting the severity score at a coming time point
• Comparing severity between treatment groups, interventions, or animal models on a common relative scale
• Defining thresholds or zones on a severity scale from the data
• Writing the severity-assessment section of an animal welfare report, a 3Rs/refinement analysis, or an application under EU Directive 2010/63/EU

For general forecasting of a time series that is not a severity score, use timesfm-forecasting or statsmodels. For study design and sample size, use experimental-design and statistical-power.

Installation

uv pip install "numpy>=1.26" "pandas>=2.0" "scipy>=1.11" "statsmodels>=0.14" matplotlib

relsa_score.py and kde_thresholds.py need only numpy/pandas/scipy; statsmodels is required for forecasting and matplotlib only for figures.