# PWR-221 — Physiological digital twin: learner worksheet Release: Revision 7T · Full Tutorial Edition Estimated time: Estimated 29 min reading and worksheet pass Difficulty: Advanced Equipment: Common household or practice equipment — The linked primary paper in the evidence notes, Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis, plus its available tables and supplement; no patient-level data or clinical decision is used.; An audit table with rows for question, inputs, omissions, version, population, comparator, calibration, uncertainty, subgroups, drift and decisions.; A calculator for supplied aggregate examples and a dated source log. Space: Room-scale practice space Automated structural checklist: 10 of 10 structural checks present Evidence quality/context: G5; Deep research depth Automated method-quality band: Comprehensive Method-rating basis: Structure coverage, instruction/check specificity, alternative distinctness, source troubleshooting, comparison depth and whether purpose/check fields are authored rather than derived. Editorial review: Pending manual editorial sign off ## Before you begin - [ ] I read the tutorial authority and stop conditions. - [ ] I have the required equipment/space or a declared accessible alternative. ## Canonical source context This research tutorial teaches how to inspect a physiological digital-twin study or product claim. You will define the clinical question, list represented and omitted variables, trace data and model versions, examine validation, calibration, subgroup errors and drift, and write a bounded verdict. No personal health data or treatment simulation is used. Target outcome: The learner produces a one-page evidence audit that identifies the twin’s target, data, omissions, validation population, prediction error, uncertainty, update rules and prohibited decisions. ### Authority and setup conditions - Choose a non-personal source that exposes methods and validation details; do not use a live clinical dashboard. - Write the exact prediction target and time horizon before reading performance claims. - Set the verdict options: research signal, internally validated, externally validated for a named context, decision-impact evidence, or insufficient. ### Required or supplied materials - The linked primary paper in the evidence notes, Development and Verification of a Digital Twin Patient Model to Predict Specific Treatment Response During the First 24 Hours of Sepsis, plus its available tables and supplement; no patient-level data or clinical decision is used. - An audit table with rows for question, inputs, omissions, version, population, comparator, calibration, uncertainty, subgroups, drift and decisions. - A calculator for supplied aggregate examples and a dated source log. ## 1. Define the target question Write the physiological outcome, population, time horizon and decision the model claims to inform. Why: “Simulate the patient” is too broad to validate. Success check: The audit has one observable target and one declared non-use. Accessible alternative: Complete the same research action — “Write the physiological outcome, population, time horizon and decision the model claims to inform.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The audit has one observable target and one declared non-use.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 2. List what the twin represents Separate measured inputs, inferred states, mechanistic equations, statistical components and outputs; list important body and context features omitted. Why: A model is a selective representation, not a complete person. Success check: Every included and omitted element is explicit enough to challenge. Accessible alternative: Complete the same research action — “Separate measured inputs, inferred states, mechanistic equations, statistical components and outputs; list important body and context features omitted.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “Every included and omitted element is explicit enough to challenge.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 3. Trace data provenance Record sites, dates, inclusion rules, missingness, coding, measurement timing, preprocessing and whether training and test people were separated. Why: Leakage and timing errors can make a model appear more accurate than it is. Success check: The source, cohort and split method are named with unresolved gaps. Accessible alternative: Complete the same research action — “Record sites, dates, inclusion rules, missingness, coding, measurement timing, preprocessing and whether training and test people were separated.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The source, cohort and split method are named with unresolved gaps.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 4. Lock the model version Record software/model version, parameters, update frequency, human rules and any changes made after seeing results. Why: A changing model cannot be interpreted from an old score without change control. Success check: The evaluated version and update rule are identifiable. Accessible alternative: Complete the same research action — “Record software/model version, parameters, update frequency, human rules and any changes made after seeing results.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The evaluated version and update rule are identifiable.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 5. Examine validation Identify internal, temporal and external validation; compare the model with a simple or existing comparator on the same cases. Why: Performance in development data does not show transport to a new setting. Success check: The audit distinguishes each validation level and names the comparator. Accessible alternative: Complete the same research action — “Identify internal, temporal and external validation; compare the model with a simple or existing comparator on the same cases.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The audit distinguishes each validation level and names the comparator.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 6. Read error and calibration Extract absolute errors, agreement or calibration across the clinically relevant range, not only a correlation or area-under-curve number. Why: A model can rank cases while systematically over- or under-predicting risk. Success check: The audit states what the reported metric means and what it omits. Accessible alternative: Complete the same research action — “Extract absolute errors, agreement or calibration across the clinically relevant range, not only a correlation or area-under-curve number.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The audit states what the reported metric means and what it omits.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 7. Check subgroups and failures Look for missing-data behaviour, coding mistakes, extreme cases, subgroup errors, uncertainty, abstention and out-of-scope cases. Why: Average performance can hide predictable harm. Success check: At least three failure modes and any untested subgroup are visible. Accessible alternative: Complete the same research action — “Look for missing-data behaviour, coding mistakes, extreme cases, subgroup errors, uncertainty, abstention and out-of-scope cases.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “At least three failure modes and any untested subgroup are visible.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 8. Test transport and drift claims Ask what changes across site, population, treatment, sensor, coding and time and how the team detects performance drift. Why: A validated model can become wrong after context changes. Success check: The audit names the revalidation triggers and whether they were tested. Accessible alternative: Complete the same research action — “Ask what changes across site, population, treatment, sensor, coding and time and how the team detects performance drift.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The audit names the revalidation triggers and whether they were tested.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## 9. Write the bounded verdict State the strongest supported evidence level and list decisions the model must not make; separate prediction performance from patient benefit. Why: Technical accuracy does not prove useful or safe care. Success check: The verdict includes uncertainty, governance gaps and the next evidence gate. Accessible alternative: Complete the same research action — “State the strongest supported evidence level and list decisions the model must not make; separate prediction performance from patient benefit.” — using speech-to-text, text-to-speech, enlarged text, keyboard-only navigation, shorter work blocks or a support person. Preserve this declared check: “The verdict includes uncertainty, governance gaps and the next evidence gate.” Do not convert the research task into capability practice. Alternative relationship: Target preserving when declared check is preserved Learner notes: ________________________________________________________________________________ Completed: [ ] ## Reflection What changed? ________________________________________________________________________________ What remains difficult? ________________________________________________________________________________ What will I repeat, adapt, ask for help with, or stop? ________________________________________________________________________________ --- Completion of this worksheet demonstrates tutorial participation only. It does not establish capability, qualification, safety clearance, diagnosis, treatment or independent validation.