<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Gaynor SM</submitter><funding>NIDCR NIH HHS</funding><funding>NINDS NIH HHS</funding><pagination>1528-1538</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8049946</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>162(5)</volume><pubmed_abstract>&lt;h4>Abstract&lt;/h4>Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts:</pubmed_abstract><journal>Pain</journal><pubmed_title>Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.</pubmed_title><pmcid>PMC8049946</pmcid><funding_grant_id>U01 DE017018</funding_grant_id><funding_grant_id>P01 NS045685</funding_grant_id><pubmed_authors>Bortsov A</pubmed_authors><pubmed_authors>Smith SB</pubmed_authors><pubmed_authors>Bair E</pubmed_authors><pubmed_authors>Greenspan JD</pubmed_authors><pubmed_authors>Park JJ</pubmed_authors><pubmed_authors>Buchheit TE</pubmed_authors><pubmed_authors>Nackley A</pubmed_authors><pubmed_authors>Gaynor SM</pubmed_authors><pubmed_authors>Fillingim RB</pubmed_authors><pubmed_authors>Ohrbach R</pubmed_authors><pubmed_authors>Tchivileva IE</pubmed_authors><pubmed_authors>Boortz-Marx RL</pubmed_authors><pubmed_authors>Whitehead W</pubmed_authors><pubmed_authors>Liedtke W</pubmed_authors><pubmed_authors>Alonso AA</pubmed_authors><pubmed_authors>Maixner W</pubmed_authors><pubmed_authors>Diatchenko L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.</name><description>&lt;h4>Abstract&lt;/h4>Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts:</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 May</publication><modification>2025-04-04T10:17:13.566Z</modification><creation>2025-04-04T10:17:13.566Z</creation></dates><accession>S-EPMC8049946</accession><cross_references><pubmed>33259458</pubmed><doi>10.1097/j.pain.0000000000002153</doi></cross_references></HashMap>