<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16(1)</volume><submitter>Ghosh A</submitter><funding>NCATS NIH HHS</funding><funding>NIDA NIH HHS</funding><funding>National Institute on Drug Abuse</funding><journal>Scientific reports</journal><pagination>1849</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12804805</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes.</pubmed_title><pmcid>PMC12804805</pmcid><funding_grant_id>K01DA063751</funding_grant_id><funding_grant_id>UL1 TR001881</funding_grant_id><funding_grant_id>K01 DA063751</funding_grant_id><pubmed_authors>Ghosh A</pubmed_authors><pubmed_authors>Shahrestani S</pubmed_authors><pubmed_authors>Moore JH</pubmed_authors><pubmed_authors>Walker CT</pubmed_authors><pubmed_authors>Orlenko A</pubmed_authors><pubmed_authors>Freda PJ</pubmed_authors><pubmed_authors>Scheer JK</pubmed_authors><pubmed_authors>Obafemi-Ajayi T</pubmed_authors></additional><is_claimable>false</is_claimable><name>Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes.</name><description/><dates><release>2026-01-01T00:00:00Z</release><publication>2026 Jan</publication><modification>2026-06-10T05:24:15.915Z</modification><creation>2026-06-10T03:07:36.756Z</creation></dates><accession>S-EPMC12804805</accession><cross_references><pubmed>41513871</pubmed><doi>10.1038/s41598-025-31453-9</doi></cross_references></HashMap>