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IntroductionGastrointestinal cancer surgery commonly leads to postoperative complications and other adverse outcomes. While prehabilitation shows promise in reducing adverse postoperative outcomes, most hospitals have resource limitations that preclude its use as standard of care. Additionally, the need to expedite surgery from diagnosis often creates a narrow window for prehabilitation initiatives. Online, self-reported screening tools may address these challenges by facilitating early identification of high-risk patients and enabling targeted preoperative interventions, thereby allowing equitable allocation of limited resources. Therefore, the primary aim of this study is to evaluate the predictive utility of a tri-modal (physical, nutritional, psychological) screening tool for patients undergoing gastrointestinal cancer surgery.MethodsThis prospective international cohort study will recruit 1214 adults undergoing elective gastrointestinal cancer surgery across 35 sites from 19 countries. Participants will complete an online screening tool developed through a comprehensive, multistep, predefined process. The screening tool comprises the Duke Activity Status Index, Patient-Generated Subjective Global Assessment Short Form and the Patient Health Questionnaire-4, in English, Spanish, French or Portuguese. These tools were selected based on a scoping review, followed by an international Delphi consensus process. The primary outcomes include rate of postoperative complications, major complications (Clavien-Dindo Classification grade III–V) and overall complication severity assessed by the Comprehensive Complications Index; all assessed 30 days postoperatively. Secondary outcomes include hospital length of stay, readmission rate within 30 days, discharge destination (home vs other), days at home and alive in 30 days postsurgery, 30-day all-cause mortality and 12-month survival. Primary analyses will establish optimal screening tool cut-points to stratify patients into clinically actionable risk categories for postoperative complications and examine the independent predictive value of these screening scores after adjusting for established clinical risk factors.Ethics and disseminationThis study has received ethical approval from the Sydney Local Health District Human Research and Ethics Committee (X25-0333 and 2025/ETH02465) and has been registered on the Open Science Framework (10.17605/OSF.IO/HVCGD). The results of Preoperative Risk Evaluation for Cancer Treatment will be submitted to reputable journals and presented at national and international conferences.

The continuous-variable firing rate (CVFR) model, widely used in neuroscience to describe the complex dynamics of excitatory biological neurons, is here trained and tested as a dynamical classifier. To this end the model is supplied with a set of attractors which are a priori embedded in the inter-node coupling matrix, via its spectral decomposition. Learning amounts to tuning the residual parameters, in order to shape a non-equilibrium path which bridges the input (the data to be classified) and the output (the target memory slot). The imposed attractors are unaltered by the training, and this enables for ex post comparisons to be eventually drawn, e.g. as it concerns the size of their associated basins of attraction. A stochastic variant of the CVFR model is also studied and found to be robust to non-targeted adversarial attacks, which corrupt with a random perturbation the items to be eventually classified. Taken as a whole, here we show that a family of biologically plausible models written in terms of coupled ODEs can efficiently cope with a non-trivial classification task.

The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model. By incorporating stable attractors into such a metapopulation model's dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks, on CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture (BERT), on IMDB, always showing high classification accuracy. These numerical evaluations illustrate that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics.

The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to describe the intertangled dynamics of excitatory biological neurons, is here trained and tested as a veritable dynamically assisted classifier. To this end the model is supplied with a set of planted attractors which are self-consistently embedded in the inter-nodes coupling matrix, via its spectral decomposition. Learning to classify amounts to sculp the basin of attraction of the imposed equilibria, directing different items towards the corresponding destination target, which reflects the class of respective pertinence. A stochastic variant of the CVFR model is also studied and found to be robust to aversarial random attacks, which corrupt the items to be classified. This remarkable finding is one of the very many surprising effects which arise when noise and dynamical attributes are made to mutually resonate.