Cross-domain fairness audit of sentiment label bias in foundation models: Comparing human and machine annotations on tweets and reviews by Blessing Ogbuokiri, George Obaido, Chioma Kamalu, Kehinde Aruleba, Okechinyere Achilonu, Ibomoiye Domor Mienye, Stephenson Echezona, Bridget Ujah-Ogbuagu, Laleh Seyyed-Kalantari

“…These results underscore both the necessity of fairness audits and the potential of lightweight mitigation strategies in sentiment classification tasks.…”
Get full text

PREDICTION OF STILLBORN PIGLETS FROM MULTIPAROUS SOWS by Daniel Alonso Domínguez-Olvera, José Guadalupe Herrera-Haro, José Ricardo Bárcena-Gama, María Esther Ortega-Cerrilla, Francisco Ernesto Martínez-Castañeda, Antonio José Rouco-Yáñez, María Angélica Ortiz-Heredia, Nathaniel Alec Rogers-Montoya

“…Results: The only factor shown to have a negative effect (p<0.01) on PSbP was litter weight at birth, while litter size at birth and parity (number of farrowings) were seen to have a positive effect (p<0.01). …”
Get full text

Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission by Narges Malakooti, Vahid Mehrnoush, Fatemeh Abdi, Mohammad Sadegh Vahidi Farashah, Fatemeh Darsareh

“…In comparison to other models, the random forest classification had the highest accuracy (0.87) and AUC (0.87) for predicting NICU admission. …”
Get full text

Intelligence in young adulthood and alcohol use disorders in a prospective cohort study of Danish men: the role of psychiatric disorders and parental psychiatric history by Erik Lykke Mortensen, Emilie Just-Østergaard, Trine Flensborg-Madsen, Joachim Knop, Holger Jelling Sørensen, Ulrik Becker

“…Information on AUD was based on diagnoses retrieved from national hospital and outpatient treatment registers, defined according to the International Classification of Diseases.Results 361 (11.0%) men were registered with AUD during follow-up. …”
Get full text

Maternal and Perinatal Outcome of Maternal Obesity at RSCM in 2014–2019 by Junita Indarti, Sulaeman Andrianto Susilo, Purnomo Hyawicaksono, Jimmy Sakti Nanda Berguna, Galuh Anindya Tyagitha, Muhammad Ikhsan

“…There were no differences in age, parity status, and family planning methods in each group of patients with different body mass index (p>0.05). …”
Get full text

Reanalysis of the Spin Direction Distribution of Galaxy Zoo SDSS Spiral Galaxies by Darius Mcadam, Lior Shamir

“…Simple binomial distribution analysis shows that the probability of the parity violation to occur by chance is lower than 0.01. …”
Get full text

Lack of association between breastfeeding duration and body mass index in children and adolescents - A Swedish cohort study. by Orsolya Haahr Vad, Lisa Önnestam, Kristina Bengtsson Boström, Åsa Jolesjö, Jenny Sandegård, Tobias Andersson

“…The adjusted analyses were adjusted for maternal age, smoking, BMI and parity, and for the child's sex, gestational age at birth and birth weight. …”
Get full text

Predictors of gestational weight gain in western India: Findings from a longitudinal study across rural and urban cohorts. by Mugdha Deshpande, Neha Kajale, Nikhil Shah, Ketan Gondhalekar, Vivek Patwardhan, Anagha Pai Raiturker, Sanjay Gupte, Leena Patankar, Jasmin Bhawra, Anuradha Khadilkar, Tarun Reddy Katapally

“…Key predictors of inadequate GWG in second and third trimesters were low socio-economic status, parity, underweight pre-gestational BMI, prenatal distress, and poor sleep. …”
Get full text

Predictors of inadequate and excessive gestational weight gain in women: a retrospective longitudinal observational study by Cheryce Harrison, Sanjeeva Ranasinha, Helena J Teede, Joanne Enticott

“…We used multinomial logistic regression to investigate associations between US National Academy of Medicine (NAM) classifications (inadequate, sufficient and excessive GWG) and BMI, age, country of birth (COB) by region, parity, socioeconomic status and visit frequency.Setting We used individual patient data routinely collected during care from one of the largest antenatal health networks in Australia.Participants The study included 17 397 women from 149 countries (based on the COB) of diverse socioeconomic backgrounds, with pregnancies between 2017 and 2021.Exposure Gestational weight gain.Results Overall, 31.5% gained below, 35.7% within and 32.8% above NAM GWG recommendations. …”
Get full text

Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics by Md Abu Sufian, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, Boumediene Hamzi

“…It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. <b>Methods</b>: The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project’s (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging. …”
Get full text