scholarly journals German Medical Data Sciences in Studies in Health Technology and Informatics – Reflections on the Fifth Volume

2021 ◽  
Author(s):  
Rainer Röhrig ◽  
Ursula Hübner ◽  
Martin Sedlmayr
Keyword(s):  
Medical Informatics ◽  
Journal Impact Factor ◽  
Peer Review Process ◽  
Health Technology ◽  
German Society ◽  
Medical Data ◽  
Health Technologies ◽  
National Medical ◽  
Journal Impact ◽  
Paper Submission

Since 2017, the German Society for Medical Informatics, Biometry and Epidemiology e.V. (GMDS) offers the submission of full papers to the annual meetings, optional in Studies in Health Technologies and Informatics (Stud HIT) or in GMS Medical Informatics, Biometrics, and Epidemiology (MIBE). GMDS’ aim is to increase the attractiveness of the conference and paper submission process in particular for young scientists and to increase the visibility of the conference. A standardized peer review process was established. Since 2017, a 25–35% of the contributions have been submitted as full papers. A total of 177 papers were published in Stud HTI. With an unofficial journal impact factor of 1.088 (2019) and 0.540 (2020), the papers were cited with a frequency similarly to national medical journals or full paper contributions of International medical informatics conferences.

Machine Learning and Data Analytics in Pervasive Health

2018 ◽  
Vol 57 (04) ◽  
pp. 194-196
Author(s):  
Nuria Oliver ◽  
Michael Marschollek ◽  
Oscar Mayora
Keyword(s):  
Machine Learning ◽  
Medical Informatics ◽  
Data Analytics ◽  
Peer Review Process ◽  
Personalised Medicine ◽  
Physiological Data ◽  
European Federation ◽  
Small Data ◽  
Health Technologies ◽  
Pervasive Health

Summary Introduction: This accompanying editorial provides a brief introduction to this focus theme, focused on “Machine Learning and Data Analytics in Pervasive Health”. Objective: The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes. Methods: A call for paper was announced to all participants of the “11th International Conference on Pervasive Computing Technologies for Healthcare”, to different working groups of the International Medical Informatics Association (IMIA) and European Federation of Medical Informatics (EFMI) and was published in June 2017 on the website of Methods of Information in Medicine. A peer review process was conducted to select the papers for this focus theme. Results: Four papers were selected to be included in this focus theme. The paper topics cover a broad range of machine learning and data analytics applications in healthcare including detection of injurious subtypes of patient-ventilator asynchrony, early detection of cognitive impairment, effective use of small data sets for estimating the performance of radiotherapy in bladder cancer treatment, and the use negation detection in and information extraction from unstructured medical texts. Conclusions: The use of machine learning and data analytics technologies in healthcare is facing a renewed impulse due to the availability of large amounts and new sources of human behavioral and physiological data, such as that captured by mobile and pervasive devices traditionally considered as nonmainstream for healthcare provision and management.

scholarly journals Lifting the lid on impact and peer review

2021 ◽  
Vol 5 ◽  
pp. 239821282110065
Author(s):  
Joseph Clift ◽  
Anne Cooke ◽  
Anthony R. Isles ◽  
Jeffrey W. Dalley ◽  
Richard N. Henson
Keyword(s):  
Impact Factor ◽  
Peer Review ◽  
Journal Impact Factor ◽  
Review Process ◽  
Peer Review Process ◽  
Journal Impact ◽  
Neuroscience Community

Brain and Neuroscience Advances has grown in tandem with the British Neuroscience Association’s campaign to build Credibility in Neuroscience, which encourages actions and initiatives aimed at improving reproducibility, reliability and openness. This commitment to credibility impacts not only what the Journal publishes, but also how it operates. With that in mind, the Editorial Board sought the views of the neuroscience community on the peer review process, and on how they should respond to the Journal Impact Factor that will be assigned to Brain and Neuroscience Advances. In this editorial, we present the results of a survey of neuroscience researchers conducted in the autumn of 2020 and discuss the broader implications of our findings for the Journal and the neuroscience community.

scholarly journals Improving the peer-review process and editorial quality: key errors escaping the review and editorial process in top scientific journals

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1670 ◽  
Author(s):  
Antoni Margalida ◽  
Mª Àngels Colomer
Keyword(s):  
Peer Review ◽  
Journal Impact Factor ◽  
Review Process ◽  
Peer Review Process ◽  
Temporal Trends ◽  
Progressive Increase ◽  
Impact Factors ◽  
Scientific Journals ◽  
Journal Impact

We apply a novel mistake index to assess trends in the proportion of corrections published between 1993 and 2014 inNature,Scienceand PNAS. The index revealed a progressive increase in the proportion of corrections published in these three high-quality journals. The index appears to be independent of the journal impact factor or the number of items published, as suggested by a comparative analyses among 16 top scientific journals of different impact factors and disciplines. A more detailed analysis suggests that the trend in the time-to-correction increased significantly over time and also differed among journals (Nature233 days;Science136 days; PNAS 232 days). A detailed review of 1,428 errors showed that 60% of corrections were related to figures, authors, references or results. According to the three categories established, 34.7% of the corrections were consideredmild, 47.7%moderateand 17.6%severe,also differing among journals. Errors occurring during the printing process were responsible for 5% of corrections inNature, 3% inScienceand 18% in PNAS. The measurement of the temporal trends in the quality of scientific manuscripts can assist editors and reviewers in identifying the most common mistakes, increasing the rigor of peer-review and improving the quality of published scientific manuscripts.

Analysis of the journal impact factor and related bibliometric indicators in education and educational research category

2021 ◽  
pp. 1-22
Author(s):  
Metin Orbay ◽  
Orhan Karamustafaoğlu ◽  
Ruben Miranda
Keyword(s):  
Impact Factor ◽  
Educational Research ◽  
Journal Impact Factor ◽  
Impact Factors ◽  
Bibliometric Indicators ◽  
Strongly Correlated ◽  
Starting Point ◽  
Consistent Assessment ◽  
The Impact ◽  
Journal Impact

This study analyzes the journal impact factor and related bibliometric indicators in Education and Educational Research (E&ER) category, highlighting the main differences among journal quartiles, using Web of Science (Social Sciences Citation Index, SSCI) as the data source. High impact journals (Q1) publish only slightly more papers than expected, which is different to other areas. The papers published in Q1 journal have greater average citations and lower uncitedness rates compared to other quartiles, although the differences among quartiles are lower than in other areas. The impact factor is only weakly negative correlated (r=-0.184) with the journal self-citation but strongly correlated with the citedness of the median journal paper (r= 0.864). Although this strong correlation exists, the impact factor is still far to be the perfect indicator for expected citations of a paper due to the high skewness of the citations distribution. This skewness was moderately correlated with the citations received by the most cited paper of the journal (r= 0.649) and the number of papers published by the journal (r= 0.484), but no important differences by journal quartiles were observed. In the period 2013–2018, the average journal impact factor in the E&ER has increased largely from 0.908 to 1.638, which is justified by the field growth but also by the increase in international collaboration and the share of papers published in open access. Despite their inherent limitations, the use of impact factors and related indicators is a starting point for introducing the use of bibliometric tools for objective and consistent assessment of researcher.

scholarly journals Estimating an EQ-5D-3L Value Set for Romania Using Time Trade-Off

2021 ◽  
Vol 18 (14) ◽  
pp. 7415
Author(s):  
Marian Sorin Paveliu ◽  
Elena Olariu ◽  
Raluca Caplescu ◽  
Yemi Oluboyede ◽  
Ileana-Gabriela Niculescu-Aron ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Assessment Process ◽  
Computer Assisted ◽  
Health State ◽  
Health States ◽  
Value Set ◽  
Health Technologies ◽  
Related Quality

Objective: To provide health-related quality of life (HRQoL) data to support health technology assessment (HTA) and reimbursement decisions in Romania, by developing a country-specific value set for the EQ-5D-3L questionnaire. Methods: We used the cTTO method to elicit health state values using a computer-assisted personal interviewing approach. Interviews were standardized following the most recent version of the EQ-VT protocol developed by the EuroQoL Foundation. Thirty EQ-5D-3L health states were randomly assigned to respondents in blocks of three. Econometric modeling was used to estimate values for all 243 states described by the EQ-5D-3L. Results: Data from 1556 non-institutionalized adults aged 18 years and older, selected from a national representative sample, were used to build the value set. All tested models were logically consistent; the final model chosen to generate the value set was an interval regression model. The predicted EQ-5D-3L values ranged from 0.969 to 0.399, and the relative importance of EQ-5D-3L dimensions was in the following order: mobility, pain/discomfort, self-care, anxiety/depression, and usual activities. Conclusions: These results can support reimbursement decisions and allow regional cross-country comparisons between health technologies. This study lays a stepping stone in the development of a health technology assessment process more driven by locally relevant data in Romania.

scholarly journals Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study

2021 ◽  
Vol 41 (4) ◽  
pp. 476-484
Author(s):  
Daniel Gallacher ◽  
Peter Kimani ◽  
Nigel Stallard
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Information Criterion ◽  
Health Technology ◽  
Weighted Averaging ◽  
Health Technologies ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

The TOP factor: An indicator of quality to complement journal impact factor

2020 ◽  
Vol 13 (3) ◽  
pp. 328-333
Author(s):  
Sven Kepes ◽  
George C. Banks ◽  
Sheila K. Keener
Keyword(s):  
Impact Factor ◽  
Journal Impact Factor ◽  
Journal Impact

The Ethics of Mobile Health Technology

2017 ◽  
Vol 41 (S1) ◽  
pp. S39-S39
Author(s):  
S. Galderisi ◽  
F. Caputo
Keyword(s):  
Health Equity ◽  
Mobile Health ◽  
New Technologies ◽  
Ethical Issues ◽  
Professional Associations ◽  
Health Technology ◽  
Ethical Guidelines ◽  
High Quality ◽  
Quality Healthcare ◽  
Health Technologies

IntroductionMobile health (m-health) technology has been growing rapidly in the last decades. The use of this technology represents an advantage, especially for reaching patients who otherwise would have no access to healthcare. However, many ethical issues arise from the use of m-health. Health equity, privacy policies, adequate informed consent and a competent, safe and high quality healthcare need to be guaranteed; professional standards and quality of doctor-patient relationship in the digital setting should not be lower than those set for in-person practice.AimsTo assess advantages and threats that may arise from the wide use of m-health technologies, in order to guarantee the application of the best medical practices, resulting in the highest quality healthcare.MethodsA literature search has been conducted to highlight the most pressing ethical issues emerging from the spreading of m-health technologies.ResultsFew ethical guidelines on the appropriate use of m-health have been developed to help clinicians adopt a professional conduct within digital settings. They focus on the need for professional associations to define ethical guidelines and for physicians to take care of their education and online behavior when using m-health technologies.ConclusionsThe rapid spreading of m-health technologies urges us to evaluate all ethical issues related to its use. It would be advisable to produce an ethical code for the use of these new technologies, to guarantee health equity, privacy protection, high quality doctor-patient relationships and to ensure that m-health is not chosen over traditional care for merely economic purposes.Disclosure of interestSG received honoraria or Advisory board/consulting fees from the following companies: Lundbeck, Janssen Pharmaceuticals, Hoffman-La Roche, Angelini-Acraf, Otsuka, Pierre Fabre and Gedeon-Richter. All other authors have declared.

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