Evidence map›Paper›PMID 34350395›Full record

ArticleJAMIA open2021

CONSENSUS: a Shiny application of dementia evaluation and reporting for the KU ADC longitudinal Clinical Cohort database.

Palash Sharma, Robert N Montgomery, Rasinio S Graves, Kayla Meyer, Suzanne L Hunt, Eric D Vidoni, Jonathan D Mahnken, Russell H Swerdlow, Jeffrey M Burns, Dinesh Pal Mudaranthakam

Open access · goldAbstract readCase Reports
In one paragraph

Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Palash SharmaDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.ORCID https://orcid.org/0000-0003-3016-7541
Robert N MontgomeryDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Rasinio S GravesUniversity of California Davis Alzheimer's Disease Center, Sacramento, California, USA.
Kayla MeyerUniversity of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.
Suzanne L HuntDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Eric D VidoniUniversity of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.
Jonathan D MahnkenDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Russell H SwerdlowUniversity of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.
Jeffrey M BurnsUniversity of Kansas Alzheimer's Disease Center, Fairway, Kansas, USA.
Dinesh Pal MudaranthakamDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
University of Kansas · USUniversity of Kansas Medical Center · USUniversity of California, Davis · US

Funding

University of Kansas Alzheimer's Disease Core CentersP30AG035982 · NIA · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BURNS, JEFFREY MURRAY · 2011 to 2020
$16.6M
NIA NIH HHS P30 AG035982
6 · The paper itself

Abstract

backgroundThe University of Kansas Alzheimer's Disease Center (KU ADC) maintains several large databases to track participant recruitment, enrollment, and capture various research-related activities. It is challenging to manage and coordinate all the research-related activities. One of the crucial activities involves generating a consensus diagnosis and communicating with participants and their primary care providers. PROCESS: To effectively manage the cohort, the KU ADC utilizes a combination of open-source electronic data capture (EDC) (i.e. REDCap), along with other homegrown data management and analytic systems developed using R-studio and Shiny. PROCESS EVALUATION: In this article, we describe the method and utility of the user-friendly dashboard that was developed for the rapid reporting of dementia evaluations which allows clinical researchers to summarize recruitment metrics, automatically generate letters to both participants and healthcare providers, which ultimately help optimize workflows.

conclusionsWe believe this general framework would be beneficial to any institution that build reports and summarizing key metrics of their research from longitudinal databases.

Indexed as

Alzheimer diseaseconsensusdata managementEDCREDCapR-studioShiny

Identifiers

PMID34350395
PMCPMC8327371
OpenAlexW3126997533

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.