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To understand how health status preceding traumatic brain injury (TBI) affects relative functional gain after inpatient rehabilitation using a data mining approach. Population-based, sex-stratified, retrospective cohort study using health administrative data from Ontario, Canada (39% of the Canadian population). Inpatient rehabilitation. Patients 14 years or older (N=5802; 63.4% male) admitted to inpatient rehabilitation within 1 year of a TBI-related acute care discharge between April 1, 2008, and March 31, 2015. Not applicable. Relative functional gain (RFG) in percentage, calculated as ([discharge FIM−admission FIM]/[126−admission FIM]×100). Health status prior to TBI was identified and internally validated using a data mining approach that categorized all International Classification of Diseases, 10th revision, codes for each patient. The average RFG was 52.8%±27.6% among male patients and 51.6%±27.1% among female patients. Sex-specific Bonferroni adjusted multivariable linear regressions identified 10 factors of preinjury health status related to neurology, emergency medicine, cardiology, psychiatry, geriatrics, and gastroenterology that were significantly associated with reduced RFG in FIM for male patients. Only 1 preinjury health status category, geriatrics, was significantly associated with RFG in female patients. Comorbid health conditions present up to 5 years preceding the TBI event were significantly associated with RFG. These findings should be considered when planning and executing interventions to maximize functional gain and to support an interdisciplinary approach. Best practices guidelines and clinical interventions for older male and female patients with TBI should be developed given the increasingly aging population with TBI. [ABSTRACT FROM AUTHOR]

Rock engineering design is currently evolving from the traditional use of safety factors and towards the more rational reliability-based design (RBD), as witnessed by the introduction of a number of geotechnical limit states design (LSD) standards worldwide. The probabilistic nature of RBD requires statistical characterization of design parameters, including the strength of intact rock. The triaxial compressive strength of intact rock is commonly characterized as a function of confining pressure, and values of the parameters that define this function are generally obtained by regression analysis of laboratory test data. However, the problem of fitting strength criteria to intact rock strength data has been historically tackled as a problem of obtaining best fit curves only, and has inadvertently omitted characterization of the variability inherent in the strength data. As a result, the uncertainty in parameter estimations resulting from this variability are often not quantified rigorously. Not only does this omission render such regression analyses incomplete, it also limits development of reliability-based design protocols for rock engineering which require statistical characterization of design parameters. To overcome both of these deficiencies, this paper presents frequentist (i.e. classical) and Bayesian regression models that rigorously incorporate variability and uncertainty associated with estimated Hoek-Brown strength parameters. In particular, it discusses the limitations of the frequentist model when dealing with limited data or a combination of tensile and compressive strength data. It discusses the potential of Bayesian data analysis techniques to overcome the issue of limited data in rock engineering design by using informative prior distributions. The paper also demonstrates the main issue in fitting strength criteria to tensile and compressive data simultaneously from a statistical perspective, and presents a Bayesian regression model that rigorously fits the strength criterion to a combination of tensile and compressive data. The paper concludes with suggested statistical approaches – Bayesian or frequentist – for conditions encountered in the analysis of intact rock strength data. [ABSTRACT FROM AUTHOR]