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BackgroundMild cognitive impairment is common in Parkinson disease (PD-MCI). However, instability in this clinical diagnosis and variability in rates of progression to dementia raises questions regarding its utility for longitudinal tracking and prediction of cognitive change in PD. We examined baseline neuropsychological test and cognitive diagnosis predictors of cognitive change in PD.MethodsPersons with PD, without dementia PD (N=138) underwent comprehensive neuropsychological assessment at baseline and were followed up to 2 years. Level II Movement Disorder Society criteria for PD-MCI and PD dementia (PDD) were applied annually. Composite global and domain cognitive z-scores were calculated based on a 10-test neuropsychological battery.ResultsBaseline diagnosis of PD-MCI was not associated with a change in global cognitive z-scores. Lower baseline attention and higher executive domain z-scores were associated with greater global cognitive z-score worsening regardless of cognitive diagnosis. Worse baseline domain z-scores in the attention and language domains were associated with progression to MCI or PDD, whereas higher baseline scores in all cognitive domains except executive function were associated with clinical and psychometric reversion to "normal" cognition.ConclusionsLower scores on cognitive tests of attention were predictive of worse global cognition over 2 years of follow-up in PD, and lower baseline attention and language scores were associated with progression to MCI or PDD. However, PD-MCI diagnosis per se was not predictive of cognitive decline over 2 years. The association between higher executive domain z-scores and greater global cognitive worsening is probably a spurious result.

Background & Objective: Biomedical text data are increasingly available for research. Tokenization is an initial step in many biomedical text mining pipelines. Tokenization is the process of parsing an input biomedical sentence (represented as a digital character sequence) into a discrete set of word/token symbols, which convey focused semantic/syntactic meaning. The objective of this study is to explore variation in tokenizer outputs when applied across a series of challenging biomedical sentences. Method: Diaz [2015] introduce 24 challenging example biomedical sentences for comparing tokenizer performance. In this study, we descriptively explore variation in outputs of eight tokenizers applied to each example biomedical sentence. The tokenizers compared in this study are the NLTK white space tokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers, Stanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers. Results: For many examples, tokenizers performed similarly effectively; however, for certain examples, there were meaningful variation in returned outputs. The white space tokenizer often performed differently than other tokenizers. We observed performance similarities for tokenizers implementing rule-based systems (e.g. pattern matching and regular expressions) and tokenizers implementing neural architectures for token classification. Oftentimes, the challenging tokens resulting in the greatest variation in outputs, are those words which convey substantive and focused biomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and (Ca2+)-regulated). Conclusion: When state-of-the-art, open-source tokenizers from Python and R were applied to a series of challenging biomedical example sentences, we observed subtle variation in the returned outputs.

Objective: To comparatively evaluate several transformer model architectures at identifying protected health information (PHI) in the i2b2/UTHealth 2014 clinical text de-identification challenge corpus. Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes obtained from N=296 patients. Using a transfer learning framework, we fine-tune several transformer model architectures on the corpus, including: BERT-base, BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During fine-tuning we vary the following model hyper-parameters: batch size, number training epochs, learning rate and weight decay. We fine tune models on a training data set, we evaluate and select optimally performing models on an independent validation dataset, and lastly assess generalization performance on a held-out test dataset. We assess model performance in terms of accuracy, precision (positive predictive value), recall (sensitivity) and F1 score (harmonic mean of precision and recall). We are interested in overall model performance (PHI identified vs. PHI not identified), as well as PHI-specific model performance. Results: We observe that the ROBERTA-large models perform best at identifying PHI in the i2b2/UTHealth 2014 corpus, achieving >99% overall accuracy and 96.7% recall/precision on the heldout test corpus. Performance was good across many PHI classes; however, accuracy/precision/recall decreased for identification of the following entity classes: professions, organizations, ages, and certain locations. Conclusions: Transformers are a promising model class/architecture for clinical text de-identification. With minimal hyper-parameter tuning transformers afford researchers/clinicians the opportunity to obtain (near) state-of-the-art performance., Comment: 38 pages, 3 figures, 6 tables, arxiv pre-print

BackgroundClinical monitoring of patients with Parkinson's disease (PD) for cognitive decline is an important element of care. The Montreal Cognitive Assessment (MoCA) has been proposed to be a sensitive tool for assessing cognitive impairment in PD. The aim of our study was to compare the responsiveness of the MoCA to decline in cognition to the responsiveness of the Mini Mental State Examination (MMSE) and the Scales for Outcomes of Parkinson's disease-cognition (SCOPA-Cog).MethodsPD patients without dementia were enrolled at 6 North American movement disorders centers between 2008 and 2011. Participants received annual evaluations including the MoCA, MMSE, and SCOPA-Cog followed by formal neuropsychological testing. The gold standard for change in cognition was defined as the change on the neuropsychological test scores over the annual assessments. The Reliable Change Method was used to provide an estimate of the probability that a given difference score would be obtained by chance. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change was quantified using receiver operating characteristics (ROC) curves.ResultsOne hundred seventeen patients were included in the analysis. Participants were followed at mean intervals of 11 ± 2 months for a median of 2 (maximum 5) visits. According to the reliable change index, 56 intervals of cognitive testing showed a decline in global cognition. ROC analysis of change in MoCA, MMSE, and SCOPA-Cog global scores compared to gold standard testing found an area under the curve (AUC) of 0.55 (95% CI 0.48-0.62), 0.56 (0.48-0.63), and 0.63 (0.55-0.70) respectively. There were no significant differences in the AUCs across the tests. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change at various thresholds for decline in scores reached a maximum of 71% for a cut-off of 1 point change on the SCOPA-Cog.ConclusionUsing neuropsychological testing as a gold standard comparator, the performance of the MoCA, MMSE, and SCOPA-Cog for d

BackgroundClinical monitoring of patients with Parkinson's disease (PD) for cognitive decline is an important element of care. The Montreal Cognitive Assessment (MoCA) has been proposed to be a sensitive tool for assessing cognitive impairment in PD. The aim of our study was to compare the responsiveness of the MoCA to decline in cognition to the responsiveness of the Mini Mental State Examination (MMSE) and the Scales for Outcomes of Parkinson's disease-cognition (SCOPA-Cog).MethodsPD patients without dementia were enrolled at 6 North American movement disorders centers between 2008 and 2011. Participants received annual evaluations including the MoCA, MMSE, and SCOPA-Cog followed by formal neuropsychological testing. The gold standard for change in cognition was defined as the change on the neuropsychological test scores over the annual assessments. The Reliable Change Method was used to provide an estimate of the probability that a given difference score would be obtained by chance. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change was quantified using receiver operating characteristics (ROC) curves.ResultsOne hundred seventeen patients were included in the analysis. Participants were followed at mean intervals of 11 ± 2 months for a median of 2 (maximum 5) visits. According to the reliable change index, 56 intervals of cognitive testing showed a decline in global cognition. ROC analysis of change in MoCA, MMSE, and SCOPA-Cog global scores compared to gold standard testing found an area under the curve (AUC) of 0.55 (95% CI 0.48-0.62), 0.56 (0.48-0.63), and 0.63 (0.55-0.70) respectively. There were no significant differences in the AUCs across the tests. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change at various thresholds for decline in scores reached a maximum of 71% for a cut-off of 1 point change on the SCOPA-Cog.ConclusionUsing neuropsychological testing as a gold standard comparator, the performance of the MoCA, MMSE, and SCOPA-Cog for d

Objective: To compare the knowledge and use of recommendations for the management of sport-related concussion (SRC) among sport and exercise medicine physicians (SEMPs) and emergency department physicians (EDPs) to assess the success of SRC knowledge transfer across Canada. Design: A self-administered, multiple-choice survey accessed via e-mail by SEMPs and EDPs. The survey had been assessed for content validity. Setting: Canada. Participants: The survey was completed between May and July 2012 by SEMPs who had passed the diploma examination of the Canadian Academy of Sport and Exercise Medicine and by EDPs who did not hold this diploma. Main outcome measures: Knowledge and identification of sources of concussion management information, use of concussion diagnosis strategies, and whether physicians use common and consistent terminology when explaining cognitive rest strategies to patients after an SRC. Results: There was a response rate of 28% (305 of 1085). The SEMP and EDP response rates were 41% (147 of 360) and 22% (158 of 725), respectively. Of the responses, 41% of EDPs and 3% of SEMPs were unaware of any consensus statements on concussion in sport; 74% of SEMPs used the Sport Concussion Assessment Tool, version 2 (SCAT2), “usually or always,” whereas 88% of EDPs never used the SCAT2. When queried about how cognitive rest could best be achieved after an SRC, no consistent answer was documented. Conclusion: Differences and a lack of consistency in the implementation of recommendations for SRC patients were identified for SEMPs and EDPs. It appears that the SCAT2 is used more in the SEMP setting than in the emergency context. Further knowledge transfer efforts and research should address the barriers to achieving more consistent advice given by physicians who attend SRC patients.

Objective: To compare the knowledge and use of recommendations for the management of sport-related concussion (SRC) among sport and exercise medicine physicians (SEMPs) and emergency department physicians (EDPs) to assess the success of SRC knowledge transfer across Canada. Design: A self-administered, multiple-choice survey accessed via e-mail by SEMPs and EDPs. The survey had been assessed for content validity. Setting: Canada. Participants: The survey was completed between May and July 2012 by SEMPs who had passed the diploma examination of the Canadian Academy of Sport and Exercise Medicine and by EDPs who did not hold this diploma. Main outcome measures: Knowledge and identification of sources of concussion management information, use of concussion diagnosis strategies, and whether physicians use common and consistent terminology when explaining cognitive rest strategies to patients after an SRC. Results: There was a response rate of 28% (305 of 1085). The SEMP and EDP response rates were 41% (147 of 360) and 22% (158 of 725), respectively. Of the responses, 41% of EDPs and 3% of SEMPs were unaware of any consensus statements on concussion in sport; 74% of SEMPs used the Sport Concussion Assessment Tool, version 2 (SCAT2), “usually or always,” whereas 88% of EDPs never used the SCAT2. When queried about how cognitive rest could best be achieved after an SRC, no consistent answer was documented. Conclusion: Differences and a lack of consistency in the implementation of recommendations for SRC patients were identified for SEMPs and EDPs. It appears that the SCAT2 is used more in the SEMP setting than in the emergency context. Further knowledge transfer efforts and research should address the barriers to achieving more consistent advice given by physicians who attend SRC patients.

BackgroundThe Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial demonstrated the effectiveness of an approach to chronic disease prevention and screening (CDPS) through a new skilled role of a 'prevention practitioner'(PP). The PP has appointments with patients 40-65 years of age that focus on primary prevention activities and screening of cancer (breast, colorectal, cervical), diabetes and cardiovascular disease and associated lifestyle factors. There are numerous and occasionally conflicting evidence-based guidelines for CDPS, and the majority of these guidelines are focused on specific diseases or conditions; however, primary care providers often attend to patients with multiple conditions. To ensure that high-level evidence guidelines were used, existing clinical practice guidelines and tools were reviewed and integrated into blended BETTER tool kits. Building on the results of the BETTER trial, the BETTER tools were updated for implementation of the BETTER 2 program into participating urban, rural and remote communities across Canada.MethodsA clinical working group consisting of PPs, clinicians and researchers with support from the Centre for Effective Practice reviewed the literature to update, revise and adapt the integrated evidence algorithms and tool kits used in the BETTER trial. These resources are nuanced, based on individual patient risk, values and preferences and are designed to facilitate decision-making between providers across the target diseases and lifestyle factors included in the BETTER 2 program. Using the updated BETTER 2 toolkit, clinicians 1) determine which CDPS actions patients are eligible to receive and 2) develop individualized 'prevention prescriptions' with patients through shared decision-making and motivational interviewing.ResultsThe tools identify the patients' risks and eligible primary CDPS activities: the patient survey captures the patient's health history; the p

BackgroundThe Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial demonstrated the effectiveness of an approach to chronic disease prevention and screening (CDPS) through a new skilled role of a 'prevention practitioner'(PP). The PP has appointments with patients 40-65 years of age that focus on primary prevention activities and screening of cancer (breast, colorectal, cervical), diabetes and cardiovascular disease and associated lifestyle factors. There are numerous and occasionally conflicting evidence-based guidelines for CDPS, and the majority of these guidelines are focused on specific diseases or conditions; however, primary care providers often attend to patients with multiple conditions. To ensure that high-level evidence guidelines were used, existing clinical practice guidelines and tools were reviewed and integrated into blended BETTER tool kits. Building on the results of the BETTER trial, the BETTER tools were updated for implementation of the BETTER 2 program into participating urban, rural and remote communities across Canada.MethodsA clinical working group consisting of PPs, clinicians and researchers with support from the Centre for Effective Practice reviewed the literature to update, revise and adapt the integrated evidence algorithms and tool kits used in the BETTER trial. These resources are nuanced, based on individual patient risk, values and preferences and are designed to facilitate decision-making between providers across the target diseases and lifestyle factors included in the BETTER 2 program. Using the updated BETTER 2 toolkit, clinicians 1) determine which CDPS actions patients are eligible to receive and 2) develop individualized 'prevention prescriptions' with patients through shared decision-making and motivational interviewing.ResultsThe tools identify the patients' risks and eligible primary CDPS activities: the patient survey captures the patient's health history; the p

BackgroundThe Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial demonstrated the effectiveness of an approach to chronic disease prevention and screening (CDPS) through a new skilled role of a 'prevention practitioner'(PP). The PP has appointments with patients 40-65 years of age that focus on primary prevention activities and screening of cancer (breast, colorectal, cervical), diabetes and cardiovascular disease and associated lifestyle factors. There are numerous and occasionally conflicting evidence-based guidelines for CDPS, and the majority of these guidelines are focused on specific diseases or conditions; however, primary care providers often attend to patients with multiple conditions. To ensure that high-level evidence guidelines were used, existing clinical practice guidelines and tools were reviewed and integrated into blended BETTER tool kits. Building on the results of the BETTER trial, the BETTER tools were updated for implementation of the BETTER 2 program into participating urban, rural and remote communities across Canada.MethodsA clinical working group consisting of PPs, clinicians and researchers with support from the Centre for Effective Practice reviewed the literature to update, revise and adapt the integrated evidence algorithms and tool kits used in the BETTER trial. These resources are nuanced, based on individual patient risk, values and preferences and are designed to facilitate decision-making between providers across the target diseases and lifestyle factors included in the BETTER 2 program. Using the updated BETTER 2 toolkit, clinicians 1) determine which CDPS actions patients are eligible to receive and 2) develop individualized 'prevention prescriptions' with patients through shared decision-making and motivational interviewing.ResultsThe tools identify the patients' risks and eligible primary CDPS activities: the patient survey captures the patient's health history

BackgroundPrimary care provides most of the evidence-based chronic disease prevention and screening services offered by the healthcare system. However, there remains a gap between recommended preventive services and actual practice. This trial (the BETTER Trial) aimed to improve preventive care of heart disease, diabetes, colorectal, breast and cervical cancers, and relevant lifestyle factors through a practice facilitation intervention set in primary care.MethodsPragmatic two-way factorial cluster RCT with Primary Care Physicians' practices as the unit of allocation and individual patients as the unit of analysis. The setting was urban Primary Care Team practices in two Canadian provinces. Eight Primary Care Team practices were randomly assigned to receive the practice-level intervention or wait-list control; 4 physicians in each team (32 physicians) were randomly assigned to receive the patient-level intervention or wait-list control. Patients randomly selected from physicians' rosters were stratified into two groups: 1) general and 2) moderate mental illness. The interventions involved a multifaceted, evidence-based, tailored practice-level intervention with a Practice Facilitator, and a patient-level intervention involving a one-hour visit with a Prevention Practitioner where patients received a tailored 'prevention prescription'. The primary outcome was a composite Summary Quality Index of 28 evidence-based chronic disease prevention and screening actions with pre-defined targets, expressed as the ratio of eligible actions at baseline that were met at follow-up. A cost-effectiveness analysis was conducted.Results789 of 1,260 (63%) eligible patients participated. On average, patients were eligible for 8.96 (SD 3.2) actions at baseline. In the adjusted analysis, control patients met 23.1% (95% CI: 19.2% to 27.1%) of target actions, compared to 28.5% (95% CI: 20.9% to 36.0%) receiving the practice-level intervention, 55.6% (95% CI: 49.0% to 62.1%) receiving the pa

BackgroundPrimary care provides most of the evidence-based chronic disease prevention and screening services offered by the healthcare system. However, there remains a gap between recommended preventive services and actual practice. This trial (the BETTER Trial) aimed to improve preventive care of heart disease, diabetes, colorectal, breast and cervical cancers, and relevant lifestyle factors through a practice facilitation intervention set in primary care.MethodsPragmatic two-way factorial cluster RCT with Primary Care Physicians' practices as the unit of allocation and individual patients as the unit of analysis. The setting was urban Primary Care Team practices in two Canadian provinces. Eight Primary Care Team practices were randomly assigned to receive the practice-level intervention or wait-list control; 4 physicians in each team (32 physicians) were randomly assigned to receive the patient-level intervention or wait-list control. Patients randomly selected from physicians' rosters were stratified into two groups: 1) general and 2) moderate mental illness. The interventions involved a multifaceted, evidence-based, tailored practice-level intervention with a Practice Facilitator, and a patient-level intervention involving a one-hour visit with a Prevention Practitioner where patients received a tailored 'prevention prescription'. The primary outcome was a composite Summary Quality Index of 28 evidence-based chronic disease prevention and screening actions with pre-defined targets, expressed as the ratio of eligible actions at baseline that were met at follow-up. A cost-effectiveness analysis was conducted.Results789 of 1,260 (63%) eligible patients participated. On average, patients were eligible for 8.96 (SD 3.2) actions at baseline. In the adjusted analysis, control patients met 23.1% (95% CI: 19.2% to 27.1%) of target actions, compared to 28.5% (95% CI: 20.9% to 36.0%) receiving the practice-level intervention, 55.6% (95% CI: 49.0% to 62.1%) receiving the pa