Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia. The present protocol consists of four distinct parts: a) an ongoing systematic review of GAI/GPT/LLM applications to understand the linked ideas, findings, and reporting standards in scholarly research, and to formulate guidelines for its use and disclosure, b) a bibliometric analysis of existing author guidelines in journals that mention GAI/GPT/LLM, with the goal of evaluating existing guidelines, analyzing the disparity in their recommendations, and identifying common rules that can be brought into the Delphi consensus process, c) a Delphi survey to establish agreement on the items for the guidelines, ensuring principled GAI/GPT/LLM use, disclosure, and reporting in academia, and d) the subsequent development and dissemination of the finalized guidelines and their supplementary explanation and elaboration documents.Comment: 20 pages, 1 figure, protoco

Bibliometric Analysis of Publisher and Journal Instructions to Authors on Generative-AI in Academic and Scientific Publishing

We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top 25 publishers. Among the top 100 journals, 70% have provided guidance on GAI. Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15 (21.4%) journals indicated their guidance exclusively applies to the writing process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of journals included specific disclosure criteria. There was variability in guidance of where to disclose the use of GAI, including in the methods, acknowledgments, cover letter, or a new section. There was also variability in how to access GAI guidance and the linking of journal and publisher instructions to authors. There is a lack of guidance by some top publishers and journals on the use of GAI by authors. Among those publishers and journals that provide guidance, there is substantial heterogeneity in the allowable uses of GAI and in how it should be disclosed, with this heterogeneity persisting among affiliated publishers and journals in some instances. The lack of standardization burdens authors and threatens to limit the effectiveness of these regulations. There is a need for standardized guidelines in order to protect the integrity of scientific output as GAI continues to grow in popularity.Comment: Pages 16, 1 figure, 2 table

An integration layer for neural simulation: PyNN in the software forest

Following the principle of separation of concerns, there is a trend in the development of neural simulation software away from monolithic simulation tools and towards an ecosystem of specialized components, each of well-defined scope, that can be combined in different combinations according to scientific need [1]. Examples of such components are CSA [2] for specifying network connectivity, NineML [3] for describing the mathematics of individual neuron and synapse models, NeuroML [4] for specifying neuronal morphology and the placement of functional elements, such as ion channels and synapses, within this morphology, Neo [5] for representing electrophysiological signals recorded from simulated neurons and synapses, NEST [6] and NEURON [7] for performing the simulations, and MUSIC [8] for facilitating runtime exchange of data between different software tools.PyNN [9] is a Python API for simulator-independent specification of spiking neuronal network models and simulation protocols. A script written in PyNN can be run on any supported simulator (or neuromorphic hardware platform) without modification. From its conception, PyNN has had an integrative role, making it easier to use multiple simulators in a single project (for cross-checking, etc.) and to port a model from one simulator to another. Recent developments have emphasized still further the potential of the PyNN approach as an integration layer, simplifying the task of gluing together different software components in order to construct a federated neural simulation platform customized to the scientific problem of interest.We report here on a number of recent enhancements to PyNN, each of which involves integration of an external software component with the PyNN API: (1) using CSA specifications of neuronal connectivity in PyNN, with automated pass-through of CSA objects to the underlying simulator, where this is supported (NEST), for efficiency; (2) import and export of NeuroML model descriptions into/from PyNN; (3) integration into PyNN of the Neo library for structured handling of electrophysiology data, greatly increasing the number of output data formats available to PyNN and making it much easier to use the same analysis/visualization tool for both simulation-derived and experimental data; (4) PyNN support for simulator-independent, user-defined neuronal and synapse models defined in NineML (rather than the fixed menu of simulator-independent models previously provided by PyNN); (5) integration into PyNN of the MUSIC library, enabling simultaneous use of multiple different simulators in a single model, defined in a single simulation script.Taken as a whole, these new features are a good illustration both of the merits of Python in general and PyNN in particular as a federation platform/integration tool for neuronal simulation, and of the benefits of a modular approach to neuroscience software development.AcknowledgementsElements of some of these efforts have been reported previously [10]. This work was supported by European Union projects FP7-269921 (BrainScaleS) and FP6-015879 (FACETS).References[1] Cornelis H, Coop AD, Bower JM (2012) A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. PLoS ONE 7(1): e28956. doi:10.1371/journal.pone.0028956[2] Djurfeldt M (2012) The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10(3):287-304. doi:10.1007/s12021-012-9146-1[3] Raikov I, Cannon R, Clewley R, Cornelis H, Davison AP, De Schutter E, Djurfeldt M, Gleeson P, Gorchetchnikov A, Plesser HE, Hill S, Hines ML, Kriener B, Le Franc Y, Lo C-C, Morrison A, Muller E, Ray S, Schwabe L, Szatmary B (2011) NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl 1): P330. doi: 10.1186/1471-2202-12-S1-P330[4] Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, et al. (2010) NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Comput Biol 6(6): e1000815. doi:10.1371/journal.pcbi.1000815[5] Davison AP, Brizzi T, Estebanez L, Jaillet F, Mahnoun Y, Rautenberg P, Sobolev A, Wachtler T, Yger P, Garcia S (2011) Neo: representing and manipulating electrophysiology data in Python. Proceedings of EuroSciPy 2011. http://pythonneuro.sciencesconf.org/903[6] Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430.[7] Carnevale, N.T. and Hines, M.L. (2006) The NEURON Book. Cambridge, UK: Cambridge University Press.[8] Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH, Ekeberg O. (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics 8(1):43-60. doi:10.1007/s12021-010-9064-z[9] Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski DA, Perrinet L and Yger P (2008) PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2:11 doi:10.3389/neuro.11.011.2008[10] Eppler JM, Djurfeldt M, Muller, E, Diesmann M, Davison AP (2012) Combining simulator independent network descriptions with run-time interoperability based on PyNN and MUSIC. Proceedings of Neuroinformatics 2012

An integration layer for neural simulation: PyNN in the software forest

Following the principle of separation of concerns, there is a trend in the development of neural simulation software away from monolithic simulation tools and towards an ecosystem of specialized components, each of well-defined scope, that can be combined in different combinations according to scientific need [1]. Examples of such components are CSA [2] for specifying network connectivity, NineML [3] for describing the mathematics of individual neuron and synapse models, NeuroML [4] for specifying neuronal morphology and the placement of functional elements, such as ion channels and synapses, within this morphology, Neo [5] for representing electrophysiological signals recorded from simulated neurons and synapses, NEST [6] and NEURON [7] for performing the simulations, and MUSIC [8] for facilitating runtime exchange of data between different software tools. PyNN [9] is a Python API for simulator-independent specification of spiking neuronal network models and simulation protocols. A script written in PyNN can be run on any supported simulator (or neuromorphic hardware platform) without modification. From its conception, PyNN has had an integrative role, making it easier to use multiple simulators in a single project (for cross-checking, etc.) and to port a model from one simulator to another. Recent developments have emphasized still further the potential of the PyNN approach as an integration layer, simplifying the task of gluing together different software components in order to construct a federated neural simulation platform customized to the scientific problem of interest. We report here on a number of recent enhancements to PyNN, each of which involves integration of an external software component with the PyNN API: (1) using CSA specifications of neuronal connectivity in PyNN, with automated pass-through of CSA objects to the underlying simulator, where this is supported (NEST), for efficiency; (2) import and export of NeuroML model descriptions into/from PyNN; (3) integration into PyNN of the Neo library for structured handling of electrophysiology data, greatly increasing the number of output data formats available to PyNN and making it much easier to use the same analysis/visualization tool for both simulation-derived and experimental data; (4) PyNN support for simulator-independent, user-defined neuronal and synapse models defined in NineML (rather than the fixed menu of simulator-independent models previously provided by PyNN); (5) integration into PyNN of the MUSIC library, enabling simultaneous use of multiple different simulators in a single model, defined in a single simulation script. Taken as a whole, these new features are a good illustration both of the merits of Python in general and PyNN in particular as a federation platform/integration tool for neuronal simulation, and of the benefits of a modular approach to neuroscience software development. Acknowledgements Elements of some of these efforts have been reported previously [10]. This work was supported by European Union projects FP7-269921 (BrainScaleS) and FP6-015879 (FACETS). References [1] Cornelis H, Coop AD, Bower JM (2012) A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. PLoS ONE 7(1): e28956. doi:10.1371/journal.pone.0028956 [2] Djurfeldt M (2012) The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10(3):287-304. doi:10.1007/s12021-012-9146-1 [3] Raikov I, Cannon R, Clewley R, Cornelis H, Davison AP, De Schutter E, Djurfeldt M, Gleeson P, Gorchetchnikov A, Plesser HE, Hill S, Hines ML, Kriener B, Le Franc Y, Lo C-C, Morrison A, Muller E, Ray S, Schwabe L, Szatmary B (2011) NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl 1): P330. doi: 10.1186/1471-2202-12-S1-P330 [4] Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, et al. (2010) NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Comput Biol 6(6): e1000815. doi:10.1371/journal.pcbi.1000815 [5] Davison AP, Brizzi T, Estebanez L, Jaillet F, Mahnoun Y, Rautenberg P, Sobolev A, Wachtler T, Yger P, Garcia S (2011) Neo: representing and manipulating electrophysiology data in Python. Proceedings of EuroSciPy 2011. http://pythonneuro.sciencesconf.org/903 [6] Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430. [7] Carnevale, N.T. and Hines, M.L. (2006) The NEURON Book. Cambridge, UK: Cambridge University Press. [8] Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH, Ekeberg O. (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics 8(1):43-60. doi:10.1007/s12021-010-9064-z [9] Davison AP, Brüderle D, Eppler JM, Kremkow J, Muller E, Pecevski DA, Perrinet L and Yger P (2008) PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2:11 doi:10.3389/neuro.11.011.2008 [10] Eppler JM, Djurfeldt M, Muller, E, Diesmann M, Davison AP (2012) Combining simulator independent network descriptions with run-time interoperability based on PyNN and MUSIC. Proceedings of Neuroinformatics 2012

The Impact of Facility Surgical Caseload Volumes on Survival Outcomes in Patients Undergoing Radical Cystectomy

The role of surgical experience and its impact on the survival requires further investigation. A cohort of patients undergoing radical cystectomy or anterior pelvic exenteration for localized bladder cancer between 2006 and 2013 at 1143 facilities across the United States was identified using the National Cancer Database and analyzed. Using overall survival (OS) as the primary outcome, the relationship between facility annual caseload (FAC) and facility annual surgical caseload (FASC) for those undergoing curative surgery was examined. Four volume groups (VG) depending on caseload using both FAC and FASC were defined. These included VG1: below 50th percentile, VG2: 50th–74th percentile, VG3: 75th–89th percentile, and VG4: 90th and above. Between 2006 and 2013, 27,272 patients underwent surgery for localized bladder cancer. The median OS was 59.66 months (95% CI: 57.79–61.77). OS improved significantly as caseload increased. The unadjusted median OS difference between VG1 and VG4 was 15.35 months (64.3 vs. 48.95 months, HR 1.19 95% CI: 1.13–1.25, p p < 0.0001) for FASC. This analysis revealed a significant and clinically important survival advantage for curative bladder cancer surgery at highly experienced centers

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7–46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data