{"id":"https://openalex.org/W7160661247","doi":"https://doi.org/10.48550/arxiv.2605.06335","title":"Eliciting associations between clinical variables from LLMs via comparison questions across populations","display_name":"Eliciting associations between clinical variables from LLMs via comparison questions across populations","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160661247","doi":"https://doi.org/10.48550/arxiv.2605.06335"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06335","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135698861","display_name":"Fabian Kabus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kabus, Fabian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135716370","display_name":"Kian Kordtomeikel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kordtomeikel, Kian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135659033","display_name":"Thomas Brox","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brox, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135714567","display_name":"Heinz Wiendl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wiendl, Heinz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135663093","display_name":"Daiana Stolz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stolz, Daiana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135638123","display_name":"Harald Binder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Binder, Harald","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.5131000280380249,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.5131000280380249,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.11969999969005585,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.04729999974370003,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.49540001153945923},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4810999929904938},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.40299999713897705},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.40209999680519104},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.39959999918937683},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.36410000920295715},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3587999939918518},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.34850001335144043}],"concepts":[{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4810999929904938},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42989999055862427},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.38749998807907104},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3732999861240387},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.36410000920295715},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.32170000672340393},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3140000104904175},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27869999408721924},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27489998936653137},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C79897977","wikidata":"https://www.wikidata.org/wiki/Q5054568","display_name":"Causal chain","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.2709999978542328},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C2780640218","wikidata":"https://www.wikidata.org/wiki/Q8277","display_name":"Multiple sclerosis","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06335","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06335","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7039186358451843}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"training":[1],"data":[2,16],"of":[3,12,53,84,99,117,197],"large":[4],"language":[5],"models":[6],"(LLMs)":[7],"comprises":[8],"a":[9,41,74,103,112,181,194,220],"wide":[10],"range":[11],"biomedical":[13],"literature,":[14],"reflecting":[15],"from":[17,216,223],"many":[18],"different":[19,133],"patient":[20,38,66],"populations.":[21],"We":[22,122,149],"investigate":[23],"how":[24,96],"it":[25],"might":[26],"be":[27],"possible":[28],"to":[29,88,128,143,226],"recover":[30,212],"information":[31,110],"on":[32,61,102,111],"correlation":[33,130],"and":[34,162,175,218],"causal":[35,139,227],"links":[36],"between":[37],"characteristics,":[39],"as":[40],"key":[42],"building":[43],"block":[44],"for":[45,77,115,132],"medical":[46],"decision":[47],"making.":[48],"To":[49],"avoid":[50],"the":[51,78,118,151,169],"pitfalls":[52],"direct":[54],"elicitation,":[55],"we":[56,94],"propose":[57],"an":[58,137],"approach":[59,142],"based":[60,101],"structured":[62],"comparison":[63,67],"questions,":[64],"specifically":[65],"triplet":[68,209],"questions.":[69],"This":[70],"is":[71],"combined":[72],"with":[73,232],"statistical":[75],"model":[76,91],"LLM":[79,233],"representation":[80],"that":[81,185,191,205,229],"provides":[82,193],"estimates":[83,131],"correlations":[85,171,225],"without":[86],"access":[87],"activations":[89],"or":[90],"internals.":[92],"Intuitively,":[93],"consider":[95],"similarity":[97],"decisions":[98],"LLMs":[100,217],"first":[104],"variable":[105,114],"are":[106,172,230],"affected":[107],"by":[108],"providing":[109],"second":[113],"one":[116],"patients":[119],"being":[120],"assessed.":[121],"then":[123],"induce":[124],"prompt-level":[125],"environment":[126],"shifts":[127],"obtain":[129,144],"subpopulations,":[134],"which":[135],"enables":[136],"invariant":[138,199],"prediction":[140],"(ICP)":[141],"conservative":[145],"candidate":[146,198],"parent":[147,200],"links.":[148,201],"demonstrate":[150],"method":[152],"in":[153,180],"two":[154],"clinical":[155],"domains,":[156],"chronic":[157],"obstructive":[158],"pulmonary":[159],"disease":[160],"(COPD)":[161],"multiple":[163],"sclerosis":[164],"(MS).":[165],"Across":[166],"prompted":[167],"environments,":[168],"elicited":[170],"smooth,":[173],"stable,":[174],"clinically":[176],"interpretable,":[177],"yet":[178],"vary":[179],"statistically":[182],"significant":[183],"way":[184],"supports":[186],"downstream":[187],"invariance":[188],"testing,":[189],"such":[190],"ICP":[192],"small":[195],"set":[196],"These":[202],"results":[203],"show":[204],"indirect":[206],"elicitation":[207],"via":[208],"comparisons":[210],"can":[211],"meaningful":[213],"association":[214],"structure":[215],"offer":[219],"cautious":[221],"route":[222],"implicit":[224],"statements":[228],"congruent":[231],"answering":[234],"patterns.":[235]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
