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OBJECTIVE
To investigate whether an earlier time to achieving and maintaining systolic blood pressure (SBP) at 120 to 140 mm Hg is associated with favorable outcomes in a cohort of patients with acute intracerebral hemorrhage.

METHODS
We pooled individual patient data from randomized controlled trials registered in the Blood Pressure in Acute Stroke Collaboration. Time was defined as time form symptom onset plus the time (hour) to first achieve and subsequently maintain SBP at 120 to 140 mm Hg over 24 hours. The primary outcome was functional status measured by the modified Rankin Scale at 90 to 180 days. A generalized linear mixed models was used, with adjustment for covariables and trial as a random effect.

RESULTS
A total of 5761 patients (mean age, 64.0 [SD, 13.0], 2120 [36.8%] females) were included in analyses. Earlier SBP control was associated with better functional outcomes …

Natural Language Processing (NLP) models optimized for predictive performance often make high confidence errors and suffer from vulnerability to adversarial and out-of-distribution data. Existing work has mainly focused on mitigation of such errors using either humans or an automated approach. In this study, we explore the usage of large language models (LLMs) for data augmentation as a potential solution to the issue of NLP models making wrong predictions with high confidence during classification tasks. We compare the effectiveness of synthetic data generated by LLMs with that of human data obtained via the same procedure. For mitigation, humans or LLMs provide natural language characterizations of high confidence misclassifications to generate synthetic data, which are then used to extend the training set. We conduct an extensive evaluation of our approach on three classification tasks and demonstrate its effectiveness in reducing the number of high confidence misclassifications present in the model, all while maintaining the same level of accuracy. Moreover, we find that the cost gap between humans and LLMs surpasses an order of magnitude, as LLMs attain human-like performance while being more scalable.

This Special Issue of the Journal of Data and Information Quality (JDIQ) contains novel theoretical and methodological contributions on data curation involving humans in the loop. In this editorial, we summarize the scope of the issue and briefly describe its content.

In order to compare the quality differences of Mume Flos at different flowering stages, 45 batches of Mume Flos at different growth and flowering stages were collected. UPLC method was used to establish a method for simultaneous determination of nine components in Mume Flos. On this basis, combined with stoichiometric analysis, 45 batches of Mume Flos were identified and classified by cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the established method could achieve good separation of each index component in the sample, and the content of phenolic acids such as neochlorogenic acid and chlorogenic acid decreases with the increase of growth period. The contents of flavonoids such as hypericin, isoquercetin, rutin, quercetin-3-O-neohesperidin were higher in the full flowering period than in other …