Background/aims
This review examined factors that delay thrombolysis and what management strategies are currently employed to minimise this delay, with the aim of suggesting future directions to ...overcome bottlenecks in treatment delivery.
Methods
A systematic review was performed according to PRISMA guidelines. The search strategy included a combination of synonyms and controlled vocabularies from Medical Subject Headings (MeSH) and EmTree covering brain ischemia, cerebrovascular accident, fibrinolytic therapy and Alteplase. The search was conducted using Medline (OVID), Embase (OVID), PubMed and Cochrane Library databases using truncations and Boolean operators. The literature search excluded review articles, trial protocols, opinion pieces and case reports. Inclusion criteria were: (1) The article directly related to thrombolysis in ischaemic stroke, and (2) The article examined at least one factor contributing to delay in thrombolytic therapy.
Results
One hundred and fifty-two studies were included. Pre-hospital factors resulted in the greatest delay to thrombolysis administration. In-hospital factors relating to assessment, imaging and thrombolysis administration also contributed. Long onset-to-needle times were more common in those with atypical, or less severe, symptoms, the elderly, patients from lower socioeconomic backgrounds, and those living alone. Various strategies currently exist to reduce delays. Processes which have achieved the greatest improvements in time to thrombolysis are those which integrate out-of-hospital and in-hospital processes, such as the Helsinki model.
Conclusion
Further integrated processes are required to maximise patient benefit from thrombolysis. Expansion of community education to incorporate less common symptoms and provision of alert pagers for patients may provide further reduction in thrombolysis times.
Since the initial publication of Cultures@SiliconValley fourteen years ago, much has changed in Silicon Valley. The corporate landscape of the Valley has shifted, with tech giants like Google, ...Facebook, LinkedIn, and Twitter vying for space with a halo of applications that connect people for work, play, romance, and education. Contingent labour has been catalyzed by ubiquitous access to the Internet on smartphones, enabling ride-sharing services like Uber and Lyft and space-sharing apps like Airbnb. Entrepreneurs compete for people's attention and screen time. Alongside these changes, daily life for all but the highest echelon has been altered by new perceptions of scarcity, risk, and shortage. The second edition of 'Cultures@SiliconValley' brings the story of technological saturation and global cultural diversity in this renowned hub of digital innovation up to the present.
Abstract
Background
Multiple sclerosis
(MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS ...should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential.
Machine learning
(ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.
Methods
Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms.
Results
Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.
Conclusions
ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.
Abstract
Background
To establish the effects of stimulating intrinsically-photosensitive retinal ganglion cells (ipRGCs) on migraine severity, and to determine if migraine produces ...objectively-measured visual field defects.
Methods
A randomized, open labelled, crossover study tested migraineurs and normal controls using multifocal pupillographic objective perimetry (mfPOP) with 44 test-regions/eye. A slow blue protocol (BP) stimulated ipRGCs, and a fast yellow protocol (YP) stimulated luminance channels. Migraine diaries assessed migraine severity. Per-region responses were analyzed according to response amplitude and time-to-peak.
Results
Thirty-eight migraineurs (42.0 ± 16.5 years, 23 females) and 24 normal controls (39.2 ± 15.2 years, 14 females) were tested. The proportion of subjects developing a migraine did not differ after either protocol, either during the 1st day (odds ratio 1.0; 95% confidence interval 0.2–4.4,
p
= 0.48) or during the first 3 days after testing (odds ratio 0.8; 95% confidence interval 0.3–2.1,
p
= 0.68). Migraine days/week did not increase following testing with either protocol in comparison to the baseline week (1.4 ± 1.6 pre-testing (mean ± SD), 1.3 ± 1.4 post-BP, and 1.3 ± 1.2 post-YP;
p
= 0.96), neither did other measures of severity. Migraine occurring up to 2 weeks before testing significantly lowered amplitudes, − 0.64 ± 0.14 dB (mean ± SE), while triptan use increased amplitudes by 0.45 ± 0.10 dB, both at
p
< 0.001.
Conclusions
Stimulating ipRGCs did not affect migraine occurrence or severity. Pupillary response characteristics were influenced by the occurrence of a recent migraine attack and a history of triptan use.
Survival of patients suffering from cerebral metastases (CM) is limited. Identification of patients with a high risk for CM is warranted to adjust follow-up care and to evaluate preventive ...strategies.
Exploratory analysis of disease-specific parameter in patients with metastatic breast cancer (MBC) treated between 1998 and 2008 using cumulative incidences and Fine and Grays’ multivariable regression analyses.
After a median follow-up of 4.0 years, 66 patients (10.5%) developed CM. The estimated probability for CM was 5%, 12% and 15% at 1, 5 and 10 years; in contrast, the probability of death without CM was 21%, 61% and 76%, respectively. A small tumor size, ER status, ductal histology, lung and lymph node metastases, human epidermal growth factor receptor 2 positive (HER2+) tumors, younger age and M0 were associated with CM in univariate analyses, the latter three being risk factors in the multivariable model. Survival was shortened in patient developing CM (24.0 months) compared with patients with no CM (33.6 months) in the course of MBC.
Young patients, primary with non-metastatic disease and HER2+ tumors, have a high risk to develop CM in MBC. Survival of patients developing CM in the course of MBC is impaired compared with patients without CM.
Since the initial publication of Cultures@SiliconValley fourteen years ago, much has changed in Silicon Valley. The corporate landscape of the Valley has shifted, with tech giants like Google, ...Facebook, LinkedIn, and Twitter vying for space with a halo of applications that connect people for work, play, romance, and education. Contingent labor has been catalyzed by ubiquitous access to the Internet on smartphones, enabling ride-sharing services like Uber and Lyft and space-sharing apps like Airbnb. Entrepreneurs compete for people's attention and screen time. Alongside these changes, daily life for all but the highest echelon has been altered by new perceptions of scarcity, risk, and shortage. Established workers and those new to the workforce try to adjust. The second edition of Cultures@SiliconValley brings the story of technological saturation and global cultural diversity in this renowned hub of digital innovation up to the present. In this fully updated edition, J. A. English-Lueck provides readers with a host of new ethnographic stories, documenting the latest expansions of Silicon Valley to San Francisco and beyond. The book explores how changes in technology, especially as mobile phones make the Internet accessible everywhere, impact work, family, and community life. The inhabitants of Silicon Valley illustrate in microcosm the social and cultural identity of the future.
We prospectively recorded CSF opening pressure in 242 adults who had a lumbar puncture with concomitant measurement of weight and height. The 95% reference interval for lumbar CSF opening pressure ...was 10 to 25 cm CSF. Body mass index had a small but clinically insignificant influence on CSF opening pressure.
Background
Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We ...aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data.
Methods
In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed.
Results
Four hundred and forty‐three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during “OFF” clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON).
Conclusions
This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.
Existing studies have analyzed many different features of postural sway in an attempt to characterize Parkinson's disease. Here, we perform a systematic literature review and find the effect size of those features collapsed across several articles. The purpose of this was to create a comprehensive list of features that have been adequately explored, what features require more investigation, and what features are yet to be explored at all.
The struggle for labor rights is often one of asserting embodied care. Workers negotiate for rest and safe physical conditions. In the United States, further embodied care is translated into health ...care and family leave benefits. In Silicon Valley, while labor still struggles in the service and manufacturing sectors, professional high‐tech work constitutes another set of challenges and expectations. Startup culture draws on the university‐student lifestyle—where institutionalized care includes a broad palette of wellness care, cafeterias, and structured recreation. So it is not surprising that yoga, massage, food, and managed fun made their way into high‐tech workplaces of the late twentieth century. Increasingly, however, that corporate care is a requirement, not a perquisite, of progressive companies recruiting elite workers. Effective care requires personal awareness and corporate surveillance in order to be effective. Corporate responsibility in Silicon Valley workplaces embraces discourses in which worker productivity and care intertwine. This care is not evenly distributed or available to all workers, but still points to an emerging set of corporate care practices. Knowledge workers are expected to work more intensively, and employers sustain them by providing care. That logic of care shaped the social experience of both care providers, such as chefs and concierges, and workers, who learn to be the subjects of such care. Based on two decades of fieldwork in companies from Apple to Yahoo, this article outlines the uneven evolution of Silicon Valley's corporate care.
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