Evaluation of a treatment-based classification algorithm for XXXX : a cross-sectional study.

@entity1 @entity1643
BACKGROUND: Several studies have investigated criteria for classifying @entity1 with @entity1643 ( @entity1643 ) into treatment-based subgroups. A comprehensive algorithm was created to translate these criteria into a clinical decision-making guide. OBJECTIVE: This study investigated the translation of the individual subgroup criteria into a comprehensive algorithm by studying the prevalence of @entity1 meeting the criteria for each treatment subgroup and the reliability of the classification. DESIGN: This was a cross-sectional, observational study. METHODS: Two hundred fifty @entity1 with acute or subacute @entity1643 were recruited from the United States and Australia to participate in the study. Trained physical therapists performed standardized assessments on all @entity1 . The researchers used these findings to classify @entity1 into subgroups. Thirty-one @entity1 were reassessed to determine interrater reliability of the algorithm decision. RESULTS: Based on individual subgroup criteria, 25.2% (95% confidence interval [CI]=19.8%-30.6%) of the @entity1 did not meet the criteria for any subgroup, 49.6% (95% CI=43.4%-55.8%) of the @entity1 met the criteria for only one subgroup, and 25.2% (95% CI=19.8%-30.6%) of the @entity1 met the criteria for more than one subgroup. The most common combination of subgroups was manipulation + specific exercise (68.4% of the @entity1 who met the criteria for 2 subgroups). Reliability of the algorithm decision was moderate (kappa=0.52, 95% CI=0.27-0.77, percentage of agreement=67%). LIMITATIONS: Due to a relatively small @entity1 sample, reliability estimates are somewhat imprecise. CONCLUSIONS: These findings provide important clinical data to guide future research and revisions to the algorithm. The finding that 25% of the @entity1 met the criteria for more than one subgroup has important implications for the sequencing of treatments in the algorithm. Likewise, the finding that 25% of the @entity1 did not meet the criteria for any subgroup provides important information regarding potential revisions to the algorithm's bottom table (which guides unclear classifications). Reliability of the algorithm is sufficient for clinical use.
