Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
6Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2432539296
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Abe Flansburg (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Skill Acquisition: 1 paper
- Training Effectiveness: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Clinician overrides are valuable preference signals, not noise; jointly learning a reward model and clinician capability from longitudinal override and outcome data can align clinical AI to patient trajectories and prevent the systematic suppression of correct-but-difficult recommendations. Value-based chronic care—with dense longitudinal outcomes and natural capability variation—offers especially favorable conditions for this approach.arxiv | Abe Flansburg provider id |
2026-04-30 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.