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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.

Additional file 1. SPIRIT Checklist

Additional file 2. World Health Organization Trial Registration Data Set

Table S3. Tobit Regression Model for Usual Care. Table detailing the starting value adjusted univariate and final multivariable for the P3P group. (DOCX 17 kb)

Table S3. Tobit Regression Model for Usual Care. Table detailing the starting value adjusted univariate and final multivariable for the P3P group. (DOCX 17 kb)

Table S2. Tobit Regression Model for P3P Intervention- All Values

Table S2. Tobit Regression Model for P3P Intervention- All Values

Table S1. Actual, Transformed and Back-Transformed Willingness to Pay values for P3P and Usual Care Groups by High or Low Starting Values. Summary of the WTP values for each study group; new table requested by reviewers. (DOCX 13 kb)

Table S1. Actual, Transformed and Back-Transformed Willingness to Pay values for P3P and Usual Care Groups by High or Low Starting Values. Summary of the WTP values for each study group; new table requested by reviewers. (DOCX 13 kb)

This paper presents a new force-sensitive surface designedfor playing music. A prototype system has been implemented using a passive capacitive sensor, a commodity multichannel audio interface, and decoding software running ona laptop computer. This setup has been a successful, lowcost route to a number of experiments in intimate musicalcontrol.

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.

A music information retrieval system can extract information that arises from how various sound sources are panned between channels during the mixing and recording process. The authors propose augmenting standard audio features, which are based on the source music, with one of two methods for extracting panning and contrast features. These additional features provide statistically important information for nontrivial audio classifications tasks. Traditional classifications focus on information about pitch, rhythm, and timbre. Other types of mixing parameters are proposed for future work.