Predictive normative modeling of neurocognitive performance in Irish jockeys using regression and neural quantile methods

This study presents a comprehensive evaluation of regression-based methods for generating normative data for neuropsychological test scores, using retrospective cross-sectional data from Irish jockeys between 2010 and 2024. The analysis focuses on four key cognitive assessments used in concussion protocols: Digit Span Forward and Digit Span Backward (assessing working memory), Speed of Comprehension from the Speed and Language Processing battery (assessing language processing speed), and the Symbol Digit Modalities Test (assessing attention and processing speed). In addition to the widely used linear regression (LR) approach, we evaluate conditional percentile estimation using both linear quantile regression (LQR) and a neural network-based quantile regression (NNQR) framework with monotonicity constraints across quantile levels which has not been previously applied in the context of normative modeling to the best of our knowledge.

Results highlight that percentile thresholds vary substantially depending on the chosen method and the covariates for corresponding test score under consideration. This variation underscores the importance of selecting an appropriate modeling approach based on the clinical scenario and the specific percentile range of interest. To make this work more viable in a practical sense, we provide an interactive dashboard that estimates normative percentiles using pre-trained LR, LQR, and NNQR models based on age, education, and sex.

It automatically highlights the best-performing method based on coverage offset analysis and supports detailed report generation. While the dashboard operates on pre-trained models for ease of use, the underlying code is open-source and available via GitHub, allowing researchers and clinicians to customize it with their own datasets and model configurations for more tailored normative estimation. This work was supported by Health Research Board (HRB), Ireland under Grant Number SDAP-2023-019.

The funding body had no role in the design of the study, data collection, analysis, interpretation of data, or writing of the manuscript. Maqsood Hussain Shah and Davood Roshan contributed equally School of Health and Human Performance, Dublin City University, Dublin, Ireland Maqsood Hussain Shah, Alannah Reville & Siobhán O’Connor School of Mathematical and Statistical Sciences and CÚRAM, Research Ireland Centre for Medical Devices, University of Galway, Galway, Ireland Irish Horseracing Regulatory Board, Dublin, Ireland UPMC Sports Surgery Clinic, Dublin and Brentford FC, London, UK UPMC Sports Surgery Clinic, Dublin, Ireland and Auckland University of Technology, Auckland, New Zealand Correspondence to Maqsood Hussain Shah or Siobhán O’Connor. The authors declare no competing interests.

For transparency, however, it should be noted that Dr Jennifer Pugh serves as Chief Medical Officer atthe Irish Horseracing Regulatory Board (IHRB), Dr James Murray serves as Consultant Neuropsychologist at the IHRB, and Alannah Reville is a certifiedathletic therapist who works with injured jockeys in Ireland. Ethical approval for the analysis was granted by the Dublin City University Research Ethics Committee (DCUREC/2024/184). All procedures were conducted in accordance with institutional guidelines.

Individual participant consent was not required for this study as the data was fully anonymised prior to analysis. Therefore the need to obtain informed consent was waived by Dublin City University Research Ethics Committee. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.