Human Feedback Types
strongExpert Verification
Directly usable for protocol triage.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
HFEPX · Eval paper review
NPCBench: A Clinical Apprenticeship Benchmark for Multimodal Care-Pathway Reasoning in Nasopharyngeal Carcinoma
Pengkai Wang, Wei-Wei Zhang, Yan Li, Min Tang +12 more
Published
Nov 5, 2026
Citations
0
Trust level
Moderate
Usefulness score
40/100 (Low)
Extraction confidence
50% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Nov 5, 2026
Should you rely on this paper?
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this for comparison and orientation, not as your only source.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The abstract does not clearly name benchmarks or metrics.
Best use
Background context only
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly name benchmarks or metrics.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
40/100
Adjacent candidate
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Abstract
Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes. These tests score individual answers, but reveal less about whether a model can integrate subspecialty knowledge with multimodal evidence and track a patient's condition throughout a course of care. To close this gap, we introduce NPCBench, a clinical apprenticeship benchmark for nasopharyngeal carcinoma (NPC). NPCBench mirrors specialist training, from guideline and foundational knowledge to case planning, longitudinal review, CarePath integration, and open-ended consultation. It contains 4,777 component and consultation items and a 100-patient CarePath cohort with 417 scored diagnostic and care tasks. The episode-level evaluation tests whether diagnostic and care answers are jointly correct within a care pathway. By linking stepwise tasks to patient-level care trajectories, NPCBench evaluates whether models ground decisions in visual evidence, interpret changes in patient state, and meet clinical requirements in consultation. Across 30 evaluated models, we observe a composition gap: strong performance on individual tasks does not ensure jointly correct diagnostic and care answers for the same patient. Among 21 multimodal models, strict CarePath success reaches at most 23%. Paired controls and physician calibration further examine evidence grounding, longitudinal revision, and consultation judging. Overall, NPCBench broadens the evaluation of medical artificial intelligence beyond static question-answering paradigms to an apprenticeship-oriented assessment of clinical workflows, thereby offering a testbed for evidence-based and temporally coherent reasoning in oncology care.
What we could verify
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.
Evaluation Modes
missingNone explicit
Validate eval design from full paper text.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
Quality Controls
missingNot reported
No explicit QC controls found.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
Reported Metrics
missingNot extracted
No metric anchors detected.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
Rater Population
strongDomain Experts
Helpful for staffing comparability.
"Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Expert Verification
- Rater population
- Domain Experts
- Unit of annotation
- Trajectory
- Expertise required
- Medicine
Evaluation details
- Evaluation modes
- None
- Agentic eval
- Long Horizon
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Background context only
Research brief
Metadata summary
Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes.
- These tests score individual answers, but reveal less about whether a model can integrate subspecialty knowledge with multimodal evidence and track a patient's condition throughout a course of care.
- To close this gap, we introduce NPCBench, a clinical apprenticeship benchmark for nasopharyngeal carcinoma (NPC).
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- Current medical benchmarks typically break down clinical competence into isolated factual questions, single-image tasks, or static case vignettes.
- These tests score individual answers, but reveal less about whether a model can integrate subspecialty knowledge with multimodal evidence and track a patient's condition throughout a course of care.
Why it matters for eval
- To close this gap, we introduce NPCBench, a clinical apprenticeship benchmark for nasopharyngeal carcinoma (NPC).
Researcher checklist
-
Human feedback protocol is explicit
Detected: Expert Verification
-
Evaluation mode is explicit
No clear evaluation mode extracted.
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.