{"id":"https://openalex.org/W7225817666","doi":"https://doi.org/10.1038/s41598-026-80462-9","title":"Applying ranked set sampling to Gaussian mixture models of Glasgow Coma Scale scores in intracerebral hemorrhage","display_name":"Applying ranked set sampling to Gaussian mixture models of Glasgow Coma Scale scores in intracerebral hemorrhage","publication_year":2026,"publication_date":"2026-11-12","ids":{"openalex":"https://openalex.org/W7225817666","doi":"https://doi.org/10.1038/s41598-026-80462-9"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-80462-9","doi":"https://doi.org/10.1038/s41598-026-80462-9","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80462-9","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1038/s41598-026-80462-9","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135884322","display_name":"Ehsan Ahmed","orcid":"https://orcid.org/0009-0003-8733-4635","observed_orcids":["https://orcid.org/0009-0003-8733-4635"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ehsan Ahmed","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0003-8733-4635","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138412931","display_name":"Azizur Rahman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azizur Rahman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065852678","display_name":"Abdulla Yusuf","orcid":"https://orcid.org/0000-0002-0685-2381","observed_orcids":["https://orcid.org/0000-0002-0685-2381"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdullah Yusuf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9301999807357788,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9301999807357788,"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/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.019200000911951065,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.008500000461935997,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7250999808311462},{"id":"https://openalex.org/C17624336","wikidata":"https://www.wikidata.org/wiki/Q461644","display_name":"Glasgow Coma Scale","level":2,"score":0.682200014591217},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5776000022888184},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C2777094939","wikidata":"https://www.wikidata.org/wiki/Q1368943","display_name":"Intracerebral hemorrhage","level":3,"score":0.4767000079154968},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44940000772476196},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.44290000200271606},{"id":"https://openalex.org/C167135981","wikidata":"https://www.wikidata.org/wiki/Q2146302","display_name":"Retrospective cohort study","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43700000643730164},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4361000061035156},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3978999853134155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38269999623298645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3801000118255615},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.33649998903274536},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C68443243","wikidata":"https://www.wikidata.org/wiki/Q2786686","display_name":"Quartile","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.27570000290870667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1038/s41598-026-80462-9","doi":"https://doi.org/10.1038/s41598-026-80462-9","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80462-9","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-80462-9","doi":"https://doi.org/10.1038/s41598-026-80462-9","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80462-9","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Clinical":[1],"severity":[2,157,193],"scales":[3],"like":[4],"the":[5,121,134,162],"Glasgow":[6],"Coma":[7],"Scale":[8],"(GCS)":[9],"exhibit":[10],"heterogeneous":[11,211],"distributions":[12],"reflecting":[13],"latent":[14],"patient":[15],"subgroups.":[16],"Finite":[17],"mixture":[18,53,101,169,189],"models":[19,54,190],"capture":[20],"this":[21],"heterogeneity,":[22],"but":[23],"component":[24,49,185],"overlap":[25,143,174,186],"limits":[26],"interpretability.":[27],"Ranked":[28],"set":[29,85],"sampling":[30,62],"(RSS)":[31],"incorporates":[32],"auxiliary":[33,105],"ranking":[34],"information":[35],"during":[36],"sample":[37],"selection,":[38],"potentially":[39,206],"improving":[40],"separability.":[41],"This":[42],"empirical":[43],"study":[44],"evaluated":[45,98],"whether":[46],"RSS":[47,114,199],"enhances":[48],"separability":[50,138],"in":[51,187],"Gaussian":[52,100,188],"of":[55,75,210],"GCS":[56,65,156],"scores":[57,66],"compared":[58],"to":[59,160,205],"simple":[60,91],"random":[61,92],"(SRS).":[63],"Admission":[64],"from":[67],"a":[68,201],"full":[69],"consecutive":[70],"cohort":[71],"(":[72,87,94],"$$\\:N=504$$":[73],")":[74,89,96],"primary":[76],"intracerebral":[77],"hemorrhage":[78],"patients":[79],"were":[80,97,118,131],"analyzed.":[81],"A":[82,103,145,178],"retrospective":[83,163,179],"ranked":[84],"subsampling":[86,93,165,181],"$$\\:n=300$$":[88,95],"and":[90,108,125,137,171],"using":[99,133],"models.":[102],"composite":[104],"variable":[106],"(age":[107],"diabetes":[109],"mellitus)":[110],"was":[111,139,148],"used":[112],"for":[113,191],"ranking.":[115],"Component":[116],"numbers":[117],"selected":[119,149],"via":[120,141],"Bayesian":[122],"Information":[123],"Criterion":[124],"bootstrap":[126],"likelihood":[127],"ratio":[128],"tests.":[129],"Parameters":[130],"estimated":[132],"expectation-maximization":[135],"algorithm,":[136],"quantified":[140],"normalized":[142],"coefficients.":[144],"three-component":[146],"model":[147,197],"under":[150],"both":[151],"schemes,":[152],"aligning":[153],"with":[154],"established":[155],"categories.":[158],"Compared":[159],"SRS,":[161],"RSS-based":[164,180],"yielded":[166],"more":[167],"concentrated":[168],"components":[170],"substantially":[172],"reduced":[173],"between":[175],"adjacent":[176],"components.":[177],"design":[182],"produced":[183],"lower":[184],"clinical":[192],"data":[194],"without":[195],"increasing":[196],"complexity.":[198],"represents":[200],"practical,":[202],"underutilized":[203],"strategy":[204],"enhance":[207],"mixture-based":[208],"analyses":[209],"medical":[212],"outcomes.":[213]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}