Feasibility Meets Implementation Science: Narrowing the Research-To-Practice Gap for Exercise Activity in Multiple Sclerosis

Smith, James; Groen, Onno van der; Learmonth, Yvonne C.

doi:10.1177/16094069231180162

Cited by 2 publications

(1 citation statement)

References 90 publications

(164 reference statements)

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“…When these questions are addressed early in the study design, it could help pre-empt possible issues when running a large clinical trial. 112 …”

Section: Model Validationmentioning

confidence: 99%

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

van der Groen,

Ghosh,

Norman

et al. 2024

Neurorehabil Neural Repair

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Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and an emphasis on the time dependency of patient-specific predictions of UL recovery profiles. In the current paper a panel of experts provide their opinion on additional predictors and aspects of the literature that can help advance stroke outcome prediction models. Potential strategies include close attention to post-stroke data collection timeframes and adoption of individual-computerized modeling methods connected to a patient’s health record. These models should account for the non-linear and the variable recovery pattern of spontaneous neurological recovery. Additionally, input data should be extended to include cognitive, genomic, sensory, neural injury, and function measures as additional predictors of recovery. The accuracy of prediction models may be further improved by including standardized measures of outcome. Finally, we consider the potential impact of refined prediction models on healthcare costs.

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“…When these questions are addressed early in the study design, it could help pre-empt possible issues when running a large clinical trial. 112 …”

Section: Model Validationmentioning

confidence: 99%

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

van der Groen,

Ghosh,

Norman

et al. 2024

Neurorehabil Neural Repair

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“Currently flying blind” Stakeholders’ perceptions of implementing statewide population-based cancer staging at diagnosis into the Western Australian Cancer Registry: a rapid qualitative process evaluation of the WA Cancer Staging Project

Smith

Trevithick

Smith

et al. 2023

BMC Health Serv Res

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Background Cancer stage at diagnosis is essential for understanding cancer outcomes, guiding cancer control activities and healthcare services, and enabling benchmarking nationally and internationally. Yet, most cancer registries in Australia do not routinely collect this data. This study explored key stakeholders’ perceptions of implementing cancer staging utilising Natural Language Processing and Machine Learning algorithms within the Western Australian Cancer Registry. Methods Perceptions of key breast and colorectal cancer stakeholders, including registry staff, clinicians, consumers, data scientists, biostatisticians, data management, healthcare staff, and health researchers, were collected. Prospective and retrospective qualitative proformas at two-time points of the Western Australian Cancer Staging Project were employed. The Consolidated Framework for Implementation Research was used to guide data collection, analysis and interpretation embedded in a Participatory Action Research approach. Data analysis also incorporated Framework Analysis and an adapted version of grading qualitative data using a visual traffic light labelling system to highlight the levels of positivity, negativity, and implementation concern. Results Twenty-nine pre-proformas and 18 post-proformas were completed online via REDCap. The grading and visual presentation of barriers and enablers aided interpretation and reviewing predicted intervention outcomes. Of the selected constructs, complexity (the perceived difficulty of the intervention) was the strongest barrier and tension for change (the situation needing change) was the strongest enabler. Implementing cancer staging into the Western Australian Cancer Registry was considered vital. Benefits included improved knowledge and understanding of various outcomes (e.g., treatment received as per Optimum Care Pathways) and benchmarking. Barriers included compatibility issues with current systems/workflows, departmental/higher managerial support, and future sustainment. Conclusions The findings aid further review of data gaps, additional cancer streams, standardising cancer staging and future improvements. The study offers an adapted version of a rapid qualitative data collection and analytic approach for establishing barriers and enablers. The findings may also assist other population-based cancer registries considering collecting cancer stage at diagnosis.

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Feasibility Meets Implementation Science: Narrowing the Research-To-Practice Gap for Exercise Activity in Multiple Sclerosis

Cited by 2 publications

References 90 publications

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

Point of View on Outcome Prediction Models in Post-Stroke Motor Recovery

“Currently flying blind” Stakeholders’ perceptions of implementing statewide population-based cancer staging at diagnosis into the Western Australian Cancer Registry: a rapid qualitative process evaluation of the WA Cancer Staging Project

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