In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

Q: Context: To determine whether the number and location of positive surgical margins (PSMs) in radical prostatectomy (RP) surgical specimens affect biochemical recurrence (BCR) rates.', 'The locations of PSMs were recorded for 1308 consecutive men who underwent RP between October 2000 and December 2006. BCR was defined as three consecutive prostate-specific antigen (PSA) level rises with the peak level >or=0.15 ng/mL. Multivariate regression analyses were used to identify preoperative predictors of PSMs and BCR. The estimated 5-year risk of BCR was calculated using the Kaplan-Meier method.', 'In all, 128 (9.8%) men had one or more PSMs. The mean body mass index, mean preoperative serum PSA level, the distributions of clinical stage and biopsy Gleason scores, and the presence or absence of biopsy perineural invasion were significantly different between men with or with no PSMs. In multivariate analysis, baseline serum PSA level, Gleason score and perineural invasion were independent preoperative predictors of PSMs. The 5-year actuarial BCR rates were dependent on the site of the PSM (P = 0.035) and not the number of PSMs (P = 0.18). The rank order of estimated 5-year BCR rates according to the site of PSMs were base > anterior > posterolateral > apex approximately posterior.\Question: Does site of positive surgical margins influence biochemical recurrence after radical prostatectomy?

A: About half of the men with PSMs in the RP surgical specimen in our prospective series did not develop BCR. The risk of BCR was dependent on the site and not the number of PSMs. Adjuvant therapy should be considered in cases with anterior and basilar PSMs due to the very high risk of BCR.
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Q: Context: It is common in public health and epidemiology that the outcome of interest is counts of events occurrence. Analysing these data using classical linear models is mostly inappropriate, even after transformation of outcome variables due to overdispersion. Zero-adjusted mixture count models such as zero-inflated and hurdle count models are applied to count data when over-dispersion and excess zeros exist. Main objective of the current paper is to apply such models to analyse risk factors associated with human helminths (S. haematobium) particularly in a case where there's a high proportion of zero counts.", 'The data were collected during a community-based randomised control trial assessing the impact of mass drug administration (MDA) with praziquantel in Malawi, and a school-based cross sectional epidemiology survey in Zambia. Count data models including traditional (Poisson and negative binomial) models, zero modified models (zero inflated Poisson and zero inflated negative binomial) and hurdle models (Poisson logit hurdle and negative binomial logit hurdle) were fitted and compared.', 'Using Akaike information criteria (AIC), the negative binomial logit hurdle (NBLH) and zero inflated negative binomial (ZINB) showed best performance in both datasets. With regards to zero count capturing, these models performed better than other models.\Question: Do zero adjusted models with applications to analysing helminths count data?

A: This paper showed that zero modified NBLH and ZINB models are more appropriate methods for the analysis of data with excess zeros. The choice between the hurdle and zero-inflated models should be based on the aim and endpoints of the study.
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Q: Context: The study investigated the effect of supplementation with maltodextrin (CHO) alone or associated to caffeine during exercise in T2DM subjects.', 'Pilot study, using eight subjects with T2DM, aged 55±10 years, received CHO (1 g/kg) or caffeine (1.5 mg/kg) alone or associated before exercise protocol. The exercise was executed at 40% heart rate (HR) reserve for 40 min, with 10-min recovery. Blood pressure (BP) and perceived exertion scale (Borg) were checked every 2 min. Blood glucose (BG) was checked every 10 min. For statistical analysis, ANOVA test was used and the value was considered statistically significant at p <0.05.', 'The results showed that BP and HR did not change significantly among all treatments. Caffeine promoted a significant reduction in BG of 75 mg/dL (65%, p <0.05) during 40 min of exercise protocol compared to all groups.\Question: Does caffeine modify blood glucose availability during prolonged low-intensity exercise in individuals with type-2 diabetes?

A:
Supplementation with 1.5 mg/kg of caffeine reduces BG concentration during prolonged exercise in T2DM patients.
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