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Record W4409148862 · doi:10.1002/psp4.70028

Harnessing Open‐Source Solutions: Insights From the First Open Systems Pharmacology (<scp>OSP</scp>) Community Conference

2025· article· en· W4409148862 on OpenAlexaff
André Dallmann, Denise Feick, Pavel Balazki, Salih Benamara, Rolf Burghaus, Marylore Chenel, Siak‐Leng Choi, Henrik Cordes, Mariana Guimarães, Abdullah Hamadeh, Ibrahim Ince, Kathleen M. Job, Tobias Kanacher, Andreas Kovar, Lars Kuepfer, Jörg Lippert, Julia Macente, Nina Nauwelaerts, Christoph Niederalt, Sheila Annie Peters, Susana Proença, Masanobu Sato, Stephan Schaller, Jan Schlender, Annika R. P. Schneider, Erik Sjögren, Juri Solodenko, Alexander Staab, Paul Vrenken, Thomas Wendl, Wilhelmus E. A. de Witte, Donato Teutonico

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsUniversity of Waterloo
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthEuropean CommissionVlaamse regeringNational Institute of Child Health and Human DevelopmentFonds Wetenschappelijk Onderzoek
KeywordsOpen sourceContext (archaeology)Open source softwareEngineeringLibrary scienceEngineering managementPharmacologyComputer scienceMedicineSoftwareBiology

Abstract

fetched live from OpenAlex

In 2017, the free and open-source software Open Systems Pharmacology (OSP) was launched. Since then, OSP has evolved from a small community into a diverse network of stakeholders committed to advancing open-source solutions for model-informed drug development (MIDD). In this context, the first OSP Community Conference was hosted by Novartis in Basel, Switzerland, on October 7-8, 2024, which gathered over 100 attendees from more than 40 institutions. This perspective synthesizes key insights from the conference.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.005
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0050.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.118
GPT teacher head0.396
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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