ArticleJMIR research protocols2018
Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes.
Article in JMIR research protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Scientometric analysis of research productivity in clinical pharmacy and practice: a 12-year review at a Middle Eastern university.Journal of pharmaceutical policy and practice · 2025Article
- Personalizing Medicine and Technologies to Address the Experiences and Needs of People with Multiple Sclerosis.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThe eHealth initiative of the Conference and Labs of the Evaluation Forum (CLEF) has aimed since 2012 to provide researchers working on health text analytics with annual workshops, shared development challenges and tasks, benchmark datasets, and software for processing and evaluation. In 2012, it ran as a scientific workshop with the aim of establishing an evaluation lab, and since 2013, this annual workshop has been supplemented with 3 or more preceding labs each year. An evaluation lab is an activity where the participating individuals or teams' goal is to solve the same problem, typically using the same dataset in a given time frame. The overall purpose of this initiative is to support patients, their next of kin, clinical staff, health scientists, and health care policy makers in accessing, understanding, using, and authoring health information in a multilingual setting. In the CLEF eHealth 2013 to 2017 installations, the aim was to address patient-centric text processing. From 2015, the scope was also extended to aid both patients' understanding and clinicians' authoring of various types of medical content. CLEF eHealth 2017 introduced a new pilot task on technology-assisted reviews (TARs) in empirical medicine in order to support health scientists and health care policymakers' information access.
objectivesThis original research paper reports on the outcomes of the first 6 installations of CLEF eHealth from 2012 to 2017. The focus is on measuring and analyzing the scholarly influence by reviewing CLEF eHealth papers and their citations.
methodsA review and bibliometric study of the CLEF eHealth proceedings, working notes, and author-declared paper extensions were conducted. Citation content analysis was used for the publications and their citations collected from Google Scholar.
resultsAs many as 718 teams registered their interest in the tasks, leading to 130 teams submitting to the 15 tasks. A total of 184 papers using CLEF eHealth data generated 1299 citations, yielding a total scholarly citation influence of almost 963,000 citations for the 741 coauthors, and included authors from 33 countries across the world. Eight tasks produced statistically significant improvements (2, 3, and 3 times with P<.001, P=.009, and P=.04, respectively) in processing quality by at least 1 out of the top 3 methods.
conclusionsThese substantial participation numbers, large citation counts, and significant performance improvements encourage continuing to develop these technologies to address patient needs. Consequently, data and tools have been opened for future research and development, and the CLEF eHealth initiative continues to run new challenges.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.