---
license: mit
language:
- en
pretty_name: Clarus Clinical Narrative Integrity v0.1.1
tags:
- clarus
- clinical-trials
- narrative-integrity
- section-transition
- evidence-reporting
- clinical-audit
task_categories:
- text-classification
size_categories:
- 1K<n<10K
---

Clarus Clinical Narrative Integrity v0.1.1

What this dataset tests

- You track meaning as it moves across sections of a clinical paper
- You detect when claims shift strength or scope without justification
- You flag narrative drift introduced by section transitions

Scope

- One document
- Multiple internal sections
  - Abstract
  - Methods
  - Results
  - Discussion
  - Conclusion
- One publication lifecycle

How this differs from v0.1

- v0.1 tests internal consistency within a single narrative fragment
- v0.1.1 tests continuity across narrative sections
- v0.1.1 is stricter and higher resolution
- Same invariant
- Finer audit surface

Typical failure modes

- Abstract upgrades exploratory findings
- Discussion removes qualifiers stated in Results
- Conclusion reframes safety or efficacy language
- Claims migrate from descriptive to causal
- Negative findings fade across sections

What this dataset does not test

- Protocol to publication transitions
- Registry or SAP drift
- Phase-to-phase definitional changes

Task format

- Each row presents paired or sequential narrative excerpts
- The model judges whether meaning is preserved across sections
- Output is a binary choice

Label meaning

- A indicates narrative integrity failure across sections
- B indicates narrative integrity preserved

Use cases

- Automated audit of clinical manuscripts
- Journal and peer-review triage
- Detection of rhetorical inflation in reporting

Version note

- v0.1 remains valid for single-fragment integrity checks
- v0.1.1 extends coverage to section-to-section narrative transitions
- Neither version supersedes the other