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The near-miss to cross-modal commutativity.
Atten Percept Psychophys
January 2025
Department of Psychology, University of Tübingen, Schleichstr. 4, 72076, Tübingen, Germany.
This paper is a follow-up to Ellermeier, Kattner, and Raum (2021, Attention, Perception, & Psychophysics, 83, 2955-2967), and provides a reanalysis of their data on cross-modal commutativity from a Bayesian perspective, and a theory-based analysis grounded on a recently suggested extension of a global psychophysical approach to cross-modal judgments (Heller, 2021, Psychological Review, 128, 509-524). This theory assumes that stimuli are judged against respondent-generated internal references that are modality-specific and potentially role-dependent (i.e.
View Article and Find Full Text PDFThe Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.
JMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
View Article and Find Full Text PDFUsing artificial intelligence to evaluate adherence to best practices in one anastomosis gastric bypass: first steps in a real-world setting.
Surg Endosc
January 2025
Division of General Surgery, Bariatric Unit, Tel Aviv Medical Center, Affiliated to Sackler Faculty of Medicine, Tel Aviv University, 6, Weizman St, 6423906, Tel- Aviv, Israel.
Background: Safety in one anastomosis gastric bypass (OAGB) is judged by outcomes, but it seems reasonable to utilize best practices for safety, whose performance can be evaluated and therefore improved. We aimed to test an artificial intelligence-based model in real world for the evaluation of adherence to best practices in OAGB.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.
View Article and Find Full Text PDFRelationship between neutropenia caused by nanoliposomal irinotecan/fluorouracil/leucovorin and treatment outcomes in the NAPOLEON-2 study (NN-2301).
Sci Rep
January 2025
Department of Medical Oncology, Sasebo Kyosai Hospital, Sasebo, Japan.
The relationship between nanoliposomal irinotecan/fluorouracil/leucovorin (NFF) treatment outcomes and neutropenia in patients with pancreatic cancer has not been thoroughly examined. Thus, we conducted a retrospective analysis of data from patients with pancreatic cancer who were treated with NFF to investigate this relationship. Neutropenia was assessed according to the Common Terminology Criteria for Adverse Events across three cutoffs: A (grade 0 versus grade 1-4), B (grades 0-1 versus 2-4), and C (grades 0-2 versus 3-4).
View Article and Find Full Text PDFExploring condition in which people accept AI over human judgements on justified defection.
Sci Rep
January 2025
College of Policy Studies, Tsuda University, Tokyo, 151-0051, Japan.
As artificial intelligence (AI) technology is introduced into different areas of society, understanding people's willingness to accept AI decisions emerges as a critical scientific and societal issue. It is an open question whether people can accept the judgement of humans or AI in situations where they are unsure of their judgement, as in the trolley problem. Here, we focus on justified defection (non-cooperation with a bad person) in indirect reciprocity because it has been shown that people avoid judging justified defection as good or bad.
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