Protocol Design

non-tumor-mechanism-guided-diagnostic-ml

Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene res

89100Total Score
Core Capability
91 / 100
Functional Suitability
12 / 12
Reliability
10 / 12
Performance & Context
7 / 8
Agent Usability
15 / 16
Human Usability
7 / 8
Security
12 / 12
Maintainability
11 / 12
Agent-Specific
17 / 20
Medical Task
34 / 35 Passed
91Canonical input for non-tumor-mechanism-guided-diagnostic-ml
5/5
91Variant A input for non-tumor-mechanism-guided-diagnostic-ml
5/5
88Variant B input for non-tumor-mechanism-guided-diagnostic-ml
5/5
86Edge input for non-tumor-mechanism-guided-diagnostic-ml
5/5
86Stress input for non-tumor-mechanism-guided-diagnostic-ml
5/5
86Scope Boundary input for non-tumor-mechanism-guided-diagnostic-ml
5/5
86Adversarial input for non-tumor-mechanism-guided-diagnostic-ml
4/5

Veto GatesRequired pass for any deployment consideration

Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS
✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS
✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS
✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASS
Research Veto✅ PASS — Applicable
DimensionResultDetail
Scientific IntegrityPASSNo fabricated references, DOIs, PMIDs, statistical values, or clinical data detected.
Practice BoundariesPASSNo diagnostic conclusions or unapproved treatment recommendations produced.
Methodological GroundPASSNo methodological fallacies detected; ethical compliance requirements noted where applicable.
Code UsabilityN/ANo executable code generated; analysis pipeline planning only

Core Capability91 / 100 — 8 Categories

Functional Suitability
Full marks (12/12); no significant issues detected.
12 / 12
100%
Reliability
Dataset Disclaimer present; four workload configs well-defined; minor gaps in parameter specification
10 / 12
83%
Performance & Context
Strong score (7/8); minor gaps noted.
7 / 8
88%
Agent Usability
Strong score (15/16); minor gaps noted.
15 / 16
94%
Human Usability
Strong score (7/8); minor gaps noted.
7 / 8
88%
Security
Full marks (12/12); no significant issues detected.
12 / 12
100%
Maintainability
Strong score (11/12); minor gaps noted.
11 / 12
92%
Agent-Specific
Mechanism-gene restriction before ML feature space reduces arbitrary feature selection
17 / 20
85%
Core Capability Total91 / 100

Medical TaskExecution Average: 87.7 / 100 — Assertions: 34/35 Passed

91
Canonical
Canonical input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
91
Variant A
Variant A input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
88
Variant B
Variant B input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
86
Edge
Edge input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
86
Stress
Stress input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
86
Scope Boundary
Scope Boundary input for non-tumor-mechanism-guided-diagnostic-ml
5/5 ✓
86
Adversarial
Adversarial input for non-tumor-mechanism-guided-diagnostic-ml
4/5 ✓
91
Canonical✅ Pass
Canonical input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 36/40|Specialized 55/60|Total 91/100
✅A1Core assertion 1 for canonical input
✅A2Core assertion 2 for canonical input
✅A3Core assertion 3 for canonical input
✅A4Core assertion 4 for canonical input
✅A5Core assertion 5 for canonical input
Pass rate: 5 / 5
91
Variant A✅ Pass
Variant A input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 36/40|Specialized 55/60|Total 91/100
✅A1Core assertion 1 for variant a input
✅A2Core assertion 2 for variant a input
✅A3Core assertion 3 for variant a input
✅A4Core assertion 4 for variant a input
✅A5Core assertion 5 for variant a input
Pass rate: 5 / 5
88
Variant B✅ Pass
Variant B input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 35/40|Specialized 53/60|Total 88/100
✅A1Core assertion 1 for variant b input
✅A2Core assertion 2 for variant b input
✅A3Core assertion 3 for variant b input
✅A4Core assertion 4 for variant b input
✅A5Core assertion 5 for variant b input
Pass rate: 5 / 5
86
Edge✅ Pass
Edge input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 34/40|Specialized 52/60|Total 86/100
✅A1Core assertion 1 for edge input
✅A2Core assertion 2 for edge input
✅A3Core assertion 3 for edge input
✅A4Core assertion 4 for edge input
✅A5Core assertion 5 for edge input
Pass rate: 5 / 5
86
Stress✅ Pass
Stress input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 34/40|Specialized 52/60|Total 86/100
✅A1Core assertion 1 for stress input
✅A2Core assertion 2 for stress input
✅A3Core assertion 3 for stress input
✅A4Core assertion 4 for stress input
✅A5Core assertion 5 for stress input
Pass rate: 5 / 5
86
Scope Boundary✅ Pass
Scope Boundary input for non-tumor-mechanism-guided-diagnostic-ml

5/5 assertions passed.

Basic 34/40|Specialized 52/60|Total 86/100
✅A1Core assertion 1 for scope boundary input
✅A2Core assertion 2 for scope boundary input
✅A3Core assertion 3 for scope boundary input
✅A4Core assertion 4 for scope boundary input
✅A5Core assertion 5 for scope boundary input
Pass rate: 5 / 5
86
Adversarial✅ Pass
Adversarial input for non-tumor-mechanism-guided-diagnostic-ml

4/5 assertions passed.

Basic 34/40|Specialized 52/60|Total 86/100
✅A1Core assertion 1 for adversarial input
✅A2Core assertion 2 for adversarial input
✅A3Core assertion 3 for adversarial input
✅A4Core assertion 4 for adversarial input
❌A5Core assertion 5 for adversarial input
Pass rate: 4 / 5
Medical Task Total87.7 / 100

Key Strengths

  • Five study patterns provide comprehensive coverage of non-oncology mechanism-guided diagnostic ml with roc, dca, and calibration research scenarios
  • Four workload configurations (Lite/Standard/Advanced/Publication+) with recommended primary plan enable broad applicability
  • Mandatory Dataset Disclaimer before all dataset-mentioning workflow sections prevents false resource claims
  • Strictly verified literature retrieval with no fabricated references maintains scientific integrity