score-decision-pathway-just-outcome TEC-0315 · proposedScore the decision pathway, not just the outcome
Extract guideline decision trees into reference step sequences, then score a model's generated pathway by LLM-judged step-level match aggregated with dynamic time warping against the reference.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Extract guideline decision trees into reference step sequences, then score a model's generated pathway by LLM-judged step-level match aggregated with dynamic time warping against the reference. Filed by the drafting stage from Benchmarking Clinical Decision Pathway Adherence in Large Language Models (2608.26592) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Pairwise human preference study on 200 sampled pathway pairs: the metric agreed with aggregated human preference 90.0% of the time, equal to the 90.0% agreement between two annotators; results held across three aggregation methods and two judge models (Kendall's W 0.70-0.87).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
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Sources
Whether a technique works is a claim. What you file here becomes a claim about this technique under the capability it addresses, with your evidence as its source, and moves the standing above the same way a paper would.