Factors associated with AL score (multivariable regression analyses for the third follow-up). by Eric Priedemann (21175492)

Subjects:

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Predicted^* value of age at school entry (in years), by level of height-for-age. by Rabi Joël Gansaonré (20416803)

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Predicted^* value of age at school entry by height-for-age z-score and child’s sex. by Rabi Joël Gansaonré (20416803)

Predicted^* value of age at school entry by height-for-age z-score and mother’s education. by Rabi Joël Gansaonré (20416803)

Predicted^* value of age at school entry by height-for-age z-score and place of residence. by Rabi Joël Gansaonré (20416803)

Predicted^* value of age at school entry by height-for-age z-score and household socioeconomic status. by Rabi Joël Gansaonré (20416803)

Participants enrollment. by Chikondi Maluwa (20660522)

“…Among the 422 caregivers who participated in the study, 267 (63.2%) were females and mean age was 44.94 years. The baseline mean knowledge level score was 9.5 (38.0%) and rose to 21.08 (84.3%) p = 0.000 immediate post-health education and a 2.1% decrease 20.54 (82.2%) p<0.001 at week six from the immediate post health education score. …”

KAP assessment scores (n = 422). by Chikondi Maluwa (20660522)

“…Among the 422 caregivers who participated in the study, 267 (63.2%) were females and mean age was 44.94 years. The baseline mean knowledge level score was 9.5 (38.0%) and rose to 21.08 (84.3%) p = 0.000 immediate post-health education and a 2.1% decrease 20.54 (82.2%) p<0.001 at week six from the immediate post health education score. …”

Change^a in years of age at school entry according to a one-unit change in the initial value of the height-for-age z-score. by Rabi Joël Gansaonré (20416803)

Predicted^* incidence of repetition by level of height-for-age. by Rabi Joël Gansaonré (20416803)

Predicted^* incidence of dropout by level of height-for-age. by Rabi Joël Gansaonré (20416803)

AL scores stratified by sex and age cohorts at different examinations in the CARLA study. by Eric Priedemann (21175492)

Subjects:

Change^a in age at school entry [95% confidence interval]. by Rabi Joël Gansaonré (20416803)

Age at school entry, incidence of grade repetition, incidence of dropout, and baseline characteristics of participants. by Rabi Joël Gansaonré (20416803)

Shares of antibiotic substances on total prescription in UTI treatment (N39.0, N30.0) by sex and age group in 2019 (female: n = 238.670 prescriptions; male: n = 40.641 prescription... by Thomas Hanslmeier (19949694)

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Individual and mean EQ-5D VAS scores in the PP-group (0 = baseline, 1 = after the first day of treatment, 2 = after the second day of treatment, 3 = after the third day of treatmen... by Marcus Persson (20496333)

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”

AL scores at baseline and follow-ups. by Eric Priedemann (21175492)

Subjects: