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IntroductionMen diagnosed with localized prostate cancer must navigate a highly preference-sensitive decision between treatment options with varying adverse outcome profiles. We evaluated whether use of a decision support tool previously shown to decrease decisional conflict also impacted the secondary outcome of post-treatment decision regret.MethodsParticipants were randomized to receive personalized decision support via the Personal Patient Profile-Prostate or usual care prior to a final treatment decision. Symptoms were measured just before randomization and 6 months later; decision regret was measured at 6 months along with records review to ascertain treatment choices. Regression modeling explored associations between baseline variables including race and D`Amico risk, study group, and 6-month variables regret, choice, and symptoms.ResultsAt 6 months, 287 of 392 (73%) men returned questionnaires of which 257 (89%) had made a treatment choice. Of that group, 201 of 257 (78%) completely answered the regret scale. Regret was not significantly different between participants randomized to the P3P intervention compared to the control group (P = 0.360). In univariate analyses, we found that Black men, men with hormonal symptoms, and men with bowel symptoms reported significantly higher decision regret (all P < 0.01). Significant interactions were detected between race and study group (intervention vs. usual care) in the multivariable model; use of the Personal Patient Profile-Prostate was associated with significantly decreased decisional regret among Black men (P = 0.037). Interactions between regret, symptoms and treatment revealed that (1) men choosing definitive treatment and reporting no hormonal symptoms reported lower regret compared to all others; and (2) men choosing active surveillance and reporting bowel symptoms had higher regret compared to all others.ConclusionThe Personal Patient Profile-Prostate decision support tool may be most beneficial in minimizing decisional regret for Black men considering treatment options for newly-diagnosed prostate cancer.Trial registrationNCT01844999.

PurposeWe evaluated the efficacy of the web based P3P (Personal Patient Profile-Prostate) decision aid vs usual care with regard to decisional conflict in men with localized prostate cancer.Materials and methodsA randomized (1:1), controlled, parallel group, nonblinded trial was performed in 4 regions of the United States. Eligible men had clinically localized prostate cancer and an upcoming consultation, and they spoke and read English or Spanish. Participants answered questionnaires to report decision making stage, personal characteristics, concerns and preferences plus baseline symptoms and decisional conflict. A randomization algorithm allocated participants to receive tailored education and communication coaching, generic teaching sheets and external websites plus a 1-page summary to clinicians (intervention) or the links plus materials provided in clinic (usual care). Conflict outcomes and the number of consultations were measured at 1 month. Univariate and multivariable models were used to analyze outcomes.ResultsA total of 392 men were randomized, including 198 to intervention and 194 to usual care, of whom 152 and 153, respectively, returned 1-month outcomes. The mean ± SD 1-month decisional conflict scale (score range 0 to 100) was 10.9 ± 16.7 for intervention and 9.9 ± 18.0 for usual care. The multivariable model revealed significantly reduced conflict in the intervention group (-5.00, 95% CI -9.40--0.59). Other predictors of conflict included income, marital or partner status, decision status, number of consultations, clinical site and D'Amico risk classification.ConclusionsIn this multicenter trial the decision aid significantly reduced decisional conflict. Other variables impacted conflict and modified the effect of the decision aid, notably risk classification, consultations and resources. P3P is an effective adjunct for shared decision making in men with localized prostate cancer.

NGLAUBER is a system which models the scientific discovery of qualitative empirical laws. As such, it falls into the category of scientific discovery systems. However, the program can also be viewed as a conceptual clustering system since it forms classes of objects and characterizes these classes. NGLAUBER differs from existing scientific discovery and conceptual clustering systems in a number of ways: It uses an incremental method to group objects into classes; these classes are formed based on the relationships between objects rather than just the attributes of objects; the system describes the relationships between classes rather than simply describing the classes; and most importantly, NGLAUBER proposes experiments by predicting future data. The experiments help the system guide itself through the search for regularities in the data.