{"id":"https://openalex.org/W7166840232","doi":"https://doi.org/10.64898/2026.06.26.734829","title":"BOSE: A Bayesian Order Statistics-Based Estimator for Recovering the Sample Mean and Standard Deviation from the Median, Range, and/or Interquartile Range","display_name":"BOSE: A Bayesian Order Statistics-Based Estimator for Recovering the Sample Mean and Standard Deviation from the Median, Range, and/or Interquartile Range","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W7166840232","doi":"https://doi.org/10.64898/2026.06.26.734829","pmid":"https://pubmed.ncbi.nlm.nih.gov/42856122","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13648368"},"language":"en","primary_location":{"id":"doi:10.64898/2026.06.26.734829","doi":"https://doi.org/10.64898/2026.06.26.734829","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.06.26.734829","pdf_url":null,"source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.64898/2026.06.26.734829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139797866","display_name":"Wenqisi Pan","orcid":"https://orcid.org/0009-0000-3341-9877","observed_orcids":["https://orcid.org/0009-0000-3341-9877"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenqisi Pan","raw_affiliation_strings":["Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA"],"raw_orcid":"https://orcid.org/0009-0000-3341-9877","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139788404","display_name":"Zeyu Lu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I152014189","display_name":"Minnesota State University, Mankato","ror":"https://ror.org/04att9732","country_code":"US","type":"education","lineage":["https://openalex.org/I152014189","https://openalex.org/I91221267"]},{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zeyu Lu","raw_affiliation_strings":["Department of Mathematics and Statistics, Minnesota State University, Mankato, Minnesota, USA","Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Minnesota State University, Mankato, Minnesota, USA","institution_ids":["https://openalex.org/I152014189"]},{"raw_affiliation_string":"Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015211419","display_name":"Wei Jiang","orcid":"https://orcid.org/0000-0001-6120-5278","observed_orcids":["https://orcid.org/0000-0001-6120-5278"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Jiang","raw_affiliation_strings":["Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017417100","display_name":"Johan Lim","orcid":"https://orcid.org/0000-0003-3130-9820","observed_orcids":["https://orcid.org/0000-0003-3130-9820"]},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Johan Lim","raw_affiliation_strings":["Department of Statistics, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082826362","display_name":"Lin Xu","orcid":"https://orcid.org/0000-0002-0537-922X","observed_orcids":["https://orcid.org/0000-0002-0537-922X","https://orcid.org/0009-0000-7242-5655"]},"institutions":[{"id":"https://openalex.org/I867280407","display_name":"The University of Texas Southwestern Medical Center","ror":"https://ror.org/05byvp690","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I867280407"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Xu","raw_affiliation_strings":["Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA","Quantitative Biomedical Research Center, Peter O\u2019Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA","institution_ids":["https://openalex.org/I867280407"]},{"raw_affiliation_string":"Quantitative Biomedical Research Center, Peter O\u2019Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA","institution_ids":["https://openalex.org/I867280407"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100722142","display_name":"Xinlei Wang","orcid":"https://orcid.org/0000-0002-8561-6511","observed_orcids":["https://orcid.org/0000-0002-8561-6511","https://orcid.org/0000-0002-2065-1105"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xinlei Wang","raw_affiliation_strings":["Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA"],"raw_orcid":"https://orcid.org/0000-0002-8561-6511","affiliations":[{"raw_affiliation_string":"Department of Mathematics, University of Texas at Arlington, Arlington, Texas, USA","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100722142"],"corresponding_institution_ids":["https://openalex.org/I189196454"],"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/T10206","display_name":"Meta-analysis and systematic reviews","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10206","display_name":"Meta-analysis and systematic reviews","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.013799999840557575,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.0017000000225380063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-mean-estimation","display_name":"sample mean estimation","score":0.8320000171661377},{"id":"https://openalex.org/keywords/standard-deviation-estimation","display_name":"standard deviation estimation","score":0.8519999980926514},{"id":"https://openalex.org/keywords/order-statistics","display_name":"order statistics","score":0.503000020980835},{"id":"https://openalex.org/keywords/weakly-informative-priors","display_name":"weakly informative priors","score":0.9390000104904175},{"id":"https://openalex.org/keywords/nonnormal-distributions","display_name":"non-normal distributions","score":0.7540000081062317},{"id":"https://openalex.org/keywords/web-application","display_name":"web application","score":0.5789999961853027}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6636000275611877},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.6444000005722046},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6416000127792358},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5633000135421753},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5343999862670898},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.459199994802475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4562000036239624},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.38260000944137573},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C181243257","wikidata":"https://www.wikidata.org/wiki/Q1693522","display_name":"Sample mean and sample covariance","level":3,"score":0.36890000104904175},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33219999074935913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3215000033378601},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.299699991941452},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.2890999913215637},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.27140000462532043},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.2671999931335449},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.25429999828338623}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.64898/2026.06.26.734829","doi":"https://doi.org/10.64898/2026.06.26.734829","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.06.26.734829","pdf_url":null,"source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},{"id":"pmid:42856122","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42856122","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"bioRxiv : the preprint server for biology","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13648368","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13648368/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"bioRxiv","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.64898/2026.06.26.734829","doi":"https://doi.org/10.64898/2026.06.26.734829","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.06.26.734829","pdf_url":null,"source":{"id":"https://openalex.org/S4306402567","display_name":"bioRxiv (Cold Spring Harbor Laboratory)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2750212522","host_organization_name":"Cold Spring Harbor Laboratory","host_organization_lineage":["https://openalex.org/I2750212522"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"bioRxiv","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G9000909408","display_name":"Statistical and Deep Generative Modeling for Enhanced CyTOF Data Interpretation and Discovery","funder_award_id":"1r01gm160515-01","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1479881385","https://openalex.org/W1534181333","https://openalex.org/W1536497620","https://openalex.org/W1537972189","https://openalex.org/W1985710418","https://openalex.org/W2103024679","https://openalex.org/W2128655828","https://openalex.org/W2170892587","https://openalex.org/W2504767107","https://openalex.org/W2923957350","https://openalex.org/W2980637195","https://openalex.org/W3014275885","https://openalex.org/W3015552915","https://openalex.org/W3036368019","https://openalex.org/W3095760859","https://openalex.org/W3198375939","https://openalex.org/W3207446311","https://openalex.org/W4376107736","https://openalex.org/W4410802920"],"related_works":["https://openalex.org/W2923957350","https://openalex.org/W2170892587","https://openalex.org/W7154499275","https://openalex.org/W3207446311","https://openalex.org/W1534181333","https://openalex.org/W6926698721","https://openalex.org/W4416690478","https://openalex.org/W1905909645","https://openalex.org/W1537972189","https://openalex.org/W2891190308"],"abstract_inverted_index":{"In":[0],"meta-analyses":[1],"of":[2,38,70],"continuous":[3],"outcomes,":[4],"the":[5,30,48,59,67,81,86,117,127],"sample":[6,49],"mean":[7,50,87],"and":[8,33,51,88,143,156,191,207],"standard":[9],"deviation":[10],"(SD)":[11],"are":[12],"essential":[13],"for":[14,85,162,188],"synthesizing":[15],"effect":[16],"sizes":[17],"across":[18,126],"studies.":[19],"However,":[20],"clinical":[21],"studies":[22,106],"frequently":[23],"report":[24],"alternative":[25],"summary":[26],"statistics,":[27],"such":[28,39,97],"as":[29,98],"median,":[31],"quartiles,":[32],"range.":[34],"To":[35],"enable":[36],"inclusion":[37],"studies,":[40],"various":[41],"methods":[42,159,209],"have":[43],"been":[44],"proposed":[45],"to":[46,79,184,213],"estimate":[47],"SD":[52,89,124],"from":[53,151],"these":[54],"reported":[55],"summaries.":[56],"We":[57],"propose":[58],"Bayesian":[60],"Order":[61],"Statistics-based":[62],"Estimator":[63],"(BOSE),":[64],"which":[65],"leverages":[66],"joint":[68],"likelihood":[69],"observed":[71],"order":[72],"statistics":[73],"together":[74],"with":[75,112,130,146],"weakly":[76],"informative":[77],"priors":[78],"obtain":[80],"full":[82],"posterior":[83,179],"distribution":[84],"without":[90],"relying":[91],"on":[92],"computationally":[93],"intensive":[94],"iterative":[95],"procedures":[96],"Markov":[99],"chain":[100],"Monte":[101],"Carlo":[102],"algorithms.":[103],"Our":[104],"numerical":[105],"demonstrate":[107],"that":[108],"BOSE":[109,153,174,206],"performs":[110],"competitively":[111],"existing":[113],"approaches":[114],"in":[115,134,196,217],"estimating":[116],"mean,":[118],"while":[119],"providing":[120],"comparable":[121],"or":[122,148,168],"improved":[123],"estimation":[125],"evaluated":[128],"scenarios,":[129],"gains":[131],"particularly":[132],"evident":[133],"small-sample":[135],"settings.":[136],"Under":[137],"non-normal":[138],"distributions":[139,164],"including":[140],"skewed,":[141],"heavy-tailed,":[142],"bimodal":[144],"settings":[145],"mild":[147],"moderate":[149],"deviations":[150],"normality,":[152],"remains":[154],"robust":[155],"stable,":[157],"whereas":[158],"specifically":[160],"designed":[161],"skewed":[163],"may":[165],"become":[166],"unstable":[167],"even":[169],"inapplicable.":[170],"Beyond":[171],"point":[172],"estimation,":[173],"naturally":[175],"provides":[176],"empirically":[177],"validated":[178],"credible":[180],"intervals,":[181],"enabling":[182],"researchers":[183],"formally":[185],"quantify":[186],"uncertainty":[187],"study-level":[189],"estimates":[190],"make":[192],"reliable,":[193],"evidence-based":[194],"decisions":[195],"meta-analytic":[197,218],"research":[198],"synthesis.":[199],"A":[200],"publicly":[201],"accessible":[202],"web":[203],"application":[204],"implementing":[205],"competing":[208],"is":[210],"also":[211],"provided":[212],"facilitate":[214],"practical":[215],"use":[216],"research.":[219]},"counts_by_year":[],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2026-07-02T00:00:00"}