HFEPX · Eval paper review
Jian Lu, Panyu Chen, Miriam Treggiari, Robert Blessing +4 more
Published
Jul 24, 2026
Citations
0
Trust level
Moderate
Usefulness score
50/100 (Medium)
Extraction confidence
55% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Oct 18, 2026
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Secondary protocol comparison source
Use if you need
A concrete protocol example with enough signal to inform rater workflow design.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly describe the evaluation setup.
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3000 MIMIC-IV-Note discharge summaries drawn as three contiguous 1000-note batches from distinct positions in the corpus. Two clinicians independently reviewed a domain-stratified sample of 268 retained candidates; a third adjudicated disagreements. Results: Stage~1 proposed 11333 candidates; 3461 were retained after Stage~2 verification---machine-generated candidates---with at least one in 69.7% of admissions. Medication (43.2%) and diagnosis or clinical-finding (25.3%) candidates predominated. In the reviewed sample, reviewers agreed on 91.0\% of candidates, and 253 of 268 (94.4\%) were judged valid after adjudication. Representative examples from all seven domains carried direct implications for clinical reasoning or patient safety. Review also revealed recurring failure modes involving temporal reasoning, evolving diagnoses, and outpatient-prescribing conventions. Discussion: Detection is highly context-dependent: judging a flagged pair requires locating each statement and assessing whether the conflict reflects a true contradiction or missing context. We propose a graded ontology spanning strict contradiction, ambiguity, and gap, with a schema characterizing each flagged case by type, section, scope, domain, attribute, and inconsistency axis. Conclusion: This formative study establishes a methodological foundation and conceptual framework to guide subsequent validated, large-scale EHR-inconsistency analysis.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Expert Verification
Directly usable for protocol triage.
"Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale."
None explicit
Validate eval design from full paper text.
"Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale."
Adjudication
Calibration/adjudication style controls detected.
"In the reviewed sample, reviewers agreed on 91.0\% of candidates, and 253 of 268 (94.4\%) were judged valid after adjudication."
Not extracted
No benchmark anchors detected.
"Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale."
Not extracted
No metric anchors detected.
"Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale."
Domain Experts
Helpful for staffing comparability.
"Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Expert Verification
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
Detected: Adjudication
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
No metric terms extracted.
