Evidence map›Paper›PMID 39364323›Full record

ArticleThe journal of research administration2024

A METHOD FOR CREATING NIH DATA TRAINING TABLES WITH REDCAP AND NIH XTRACT.

John E Kerrigan, Sally Lu

Abstract read
In one paragraph

Article in The journal of research administration, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

John E KerriganDirector of Academic Technologies, School of Graduate Studies - Biomedical Health Sciences, Rutgers University, 56 College Ave., New Brunswick, NJ 08901 United States.
Sally LuErnest Mario School of Pharmacy, PharmD Candidate 2024, Rutgers University, New Brunswick, New Jersey, United, States.

Funding

New Jersey Alliance for Clinical Translational Science: NJ ACTSUL1TR003017 · NCATS · RUTGERS BIOMEDICAL/HEALTH SCIENCES-RBHS · PI PANETTIERI, REYNOLD ALEXANDER · 2019 to 2023
$28.9M
NCATS NIH HHS UL1 TR003017
6 · The paper itself

Abstract

A major pre-award administrative challenge research universities face is turnaround time for generation of high-quality NIH Data Training Tables for NIH training grants (e.g., T32, K12, TL1, KL2, R25s) which are required for training grant submission proposals to the National Institutes of Health (NIH). Universities with dedicated training grant submission offices generally require data preparation following a structured timeline of several months in advance of the grant submission due date, while other universities with less or no dedicated support for training grant submissions use an ad hoc approach. In these cases, department or program administrators may collect the data manually, in Excel or REDCap, or similar manually maintained methods for those tables requested by the specific NIH grant announcement for the relevant participating graduate predoctoral and/or postdoctoral (including clinical) training programs across the university, depending on the training focus and the "participating faculty" provided by the proposed program director (PD/PI) for the grant. We describe an efficient "federated" method of data collection and construction for NIH Tables (2, 4, 5A/B, 6A/B & -8A part III/8C part III) for new and renewal applications by combining the use of REDCap and NIH xTRACT, leveraging the strengths of each.

Indexed as

NIH Data Training TablesNIH Training GrantsNIH xTRACTREDCap

Identifiers

PMID39364323
PMCPMC11449351

What OpenQuestion holds

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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.