Skip to content
#protein folding Dataset Open access

Incremental predictive value of cystatin C for next-visit care needs in older adults: reproducibility code, models, and supporting materials from CHARLS

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Frailty in Older Adults

Abstract

This record provides reproducibility materials for the study “Incremental predictive value of cystatin C for next-visit care needs in older adults: a longitudinal study using CHARLS data.” The study evaluates prediction of three next-visit states: alive without basic activities of daily living (BADL) assistance, alive needing BADL assistance, and death. This updated release accompanies the manuscript revision dated 11 October 2026. It includes the original analysis workflow and additional analyses incorporating grip strength, C-reactive protein, and hemoglobin; comparisons under common regularization settings and alternative calibration approaches; sensitivity analyses; assessment-capacity comparisons; and community-based outer-fold evaluation. The materials include data-construction and analysis code, 27 saved final care-prediction models and one outcome-observation model, aggregate results and bootstrap metric draws, figure-generation code, software-environment documentation, validation records, and SHA-256 checksums. The revised manuscript and supplementary materials are provided in Word and PDF formats, together with Figures 1–5 in TIFF, PNG, PDF, and SVG formats. The release is distributed across five ZIP archives:01 — Readme and verification;02 — Original analysis and models;03 — Extended analysis and models;05 — Publication figures. Download all five archives and extract them into the same destination directory, preserving their internal paths. Together they reconstruct the CHARLS_CysC_20261011 folder. Start with README.md and run “python reproduce.py --stage verify” to check file integrity. The README and module documentation describe dependencies, required inputs, and execution instructions. CHARLS participant-level records, derived individual-level panels, individual predictions, and participant-specific fold assignments are not distributed. Full analysis reproduction requires appropriately authorized CHARLS data. The package provides code for data preparation, model fitting, calibration, evaluation, resampling, and figure generation; saved-model application is also supported. Validation records distinguish executed data reconstruction, model replay, aggregate-result checks, and figure reproduction from complete model refitting. The release includes the full fitting and resampling code, but packaging validation did not repeat every historical model fit and bootstrap analysis. Historical components retained for provenance are identified in the README; the current publication files are in the manuscript and figures directory.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.