RTT Medical Operator Lab — Instructor Version

Instructor Materials for the Tri‑Regime Medical Module#

RTT/1‑Aligned • Zero Drift • Structural • Student‑Safe#

Instructor Overview#

This lab teaches students how to run the five medical operators:

1. MEDICAL_METADATA_OPERATOR
2. MEDICAL_DRIFT_OPERATOR
3. MEDICAL_SUBSTRATE_OPERATOR
4. MEDICAL_REGIME_OPERATOR
5. MEDICAL_CONTINUITY_OPERATOR
6. MEDICAL_SYNTHESIS_OPERATOR (capstone)

Students work with three medical regimes:

• Cleveland Clinic (US clinical)
• Ping An Good Doctor (China AI‑augmented)
• NHS.uk (UK public‑health)

The goal is to teach:

• drift awareness
• substrate literacy
• regime differences
• continuity kernel extraction
• patient‑safe synthesis

Students never produce medical advice.
They produce structural clarity.

Instructor Notes (Global)#

• Students must work with structural elements only (symptoms, risk factors, red flags, escalation logic).
• Students must never copy text from real medical pages.
• Students must use the example JSON packets provided in /docs/medicine/examples/.
• Students must treat Ping An as high‑drift, translation‑unstable, AI‑augmented, and commercially influenced.
• Students must treat NHS as very low drift, clarity‑first, and public‑health oriented.
• Students must treat Cleveland Clinic as clinical baseline.
• Students must always disclose uncertainty and escalation indicators.
• Students must never collapse regime differences into a single narrative.
• Students must always identify drift sources before synthesizing.

Lab Structure (Instructor Version)#

Each step below includes:

• Student Task
• Instructor Expectations
• Common Pitfalls
• Grading Notes

STEP 1 — MEDICAL_METADATA_OPERATOR#

Identify substrate, noise, translation layers, AI overlays, commercial layers.#

Student Task#

Students ingest the three example JSON files and extract:

• substrate type
• substrate stability
• noise level
• translation stability
• commercial overlays
• AI augmentation
• accessibility stability
• drift sensitivity
• regime hint

Instructor Expectations#

Students should correctly identify:

Common Pitfalls#

• Treating Ping An as stable
• Missing translation drift
• Missing commercial overlays
• Assuming all HTML is equal
• Forgetting accessibility stability

Grading Notes#

Full credit requires:

• correct substrate classification
• correct noise identification
• correct overlay detection
• correct regime hint

STEP 2 — MEDICAL_DRIFT_OPERATOR#

Detect template, semantic, translation, AI, commercial, and regime drift.#

Student Task#

Students compute drift maps for each regime using the example packets.

Instructor Expectations#

• Cleveland Clinic → low drift
• Ping An → high drift
• NHS → very low drift

Students must identify:

• template drift
• semantic drift
• translation drift
• AI drift
• commercial drift
• regime drift

Common Pitfalls#

• Underestimating Ping An drift
• Missing AI‑generated instability
• Missing commercial drift
• Treating NHS as “no drift” instead of “very low drift”

Grading Notes#

Full credit requires:

• correct drift classification
• correct drift flags
• correct drift score reasoning

STEP 3 — MEDICAL_SUBSTRATE_OPERATOR#

Evaluate substrate stability, interference, and risk.#

Student Task#

Students evaluate:

• substrate stability
• substrate noise
• layer interference
• accessibility stability
• translation/commercial/AI layers

Instructor Expectations#

• Cleveland Clinic → stable, low noise
• Ping An → unstable, high interference
• NHS → very stable, minimal interference

Common Pitfalls#

• Ignoring accessibility
• Treating mobile_html as equivalent to html
• Missing interference layers

Grading Notes#

Full credit requires:

• correct substrate packet
• correct interference identification
• correct substrate score

STEP 4 — MEDICAL_REGIME_OPERATOR#

Classify regime logic and detect regime coherence or shifts.#

Student Task#

Students classify each example into:

• clinical regime
• AI‑augmented commercial regime
• public‑health regime

Instructor Expectations#

• Cleveland Clinic → clinical
• Ping An → AI‑augmented commercial
• NHS → public‑health

Students must identify:

• regime signals
• regime coherence
• regime shifts

Common Pitfalls#

• Treating Ping An as clinical
• Missing regime shifts
• Overlooking public‑health framing

Grading Notes#

Full credit requires:

• correct regime classification
• correct regime signals
• correct coherence assessment

STEP 5 — MEDICAL_CONTINUITY_OPERATOR#

Extract the continuity kernel across regimes.#

Student Task#

Students compute:

• stable symptoms
• stable risk factors
• stable red flags
• stable actions
• stable differentials

Instructor Expectations#

Students must produce a regime‑invariant continuity kernel.

Expected stable elements:

• symptoms: discomfort, pressure, shortness of breath
• risks: age, chronic illness, hypertension
• red flags: severe pain, difficulty breathing, fainting
• actions: emergency escalation for red flags
• differentials: cardiac, pulmonary, musculoskeletal

Common Pitfalls#

• Including regime‑specific elements
• Including AI‑generated suggestions
• Including commercial prompts
• Including translation artifacts

Grading Notes#

Full credit requires:

• correct continuity kernel
• correct exclusion of drift‑sensitive elements

STEP 6 — MEDICAL_SYNTHESIS_OPERATOR#

Produce a drift‑bounded, regime‑aware, patient‑safe synthesis.#

Student Task#

Students integrate all upstream packets to produce:

• stable elements
• unstable elements
• regime‑specific elements
• drift‑sensitive elements
• substrate risks
• uncertainty flags
• escalation indicators
• patient‑safe summary

Instructor Expectations#

Students must:

• preserve continuity kernel
• separate regime‑specific differences
• disclose uncertainty
• identify escalation indicators
• avoid medical advice
• avoid collapsing regimes

Common Pitfalls#

• Writing medical advice
• Collapsing regimes into one narrative
• Ignoring drift
• Ignoring substrate risks
• Missing uncertainty disclosure

Grading Notes#

Full credit requires:

• correct synthesis packet
• correct regime separation
• correct uncertainty disclosure
• correct escalation indicators
• patient‑safe summary

Instructor Rubric (10‑Point Scale)#

A score of 9–10 indicates full operator literacy.

Instructor Closing Notes#

This lab trains students to:

• see medical information structurally
• identify drift and instability
• understand regime differences
• extract continuity kernels
• produce safe, stable, cross‑regime synthesis

It is the medical equivalent of the archive_org operator lab — but tuned for:

• multilingual drift
• AI‑generated instability
• commercial overlays
• public‑health framing
• clinical conservatism