Evidence map›Paper›PMID 33820939›Full record

ArticleScientific reports2021

A new open-access platform for measuring and sharing mTBI data.

August G Domel, Samuel J Raymond, Chiara Giordano, Yuzhe Liu, Seyed Abdolmajid Yousefsani, Michael Fanton, Nicholas J Cecchi, Olga Vovk, Ileana Pirozzi, Ali Kight and 17 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

9 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. On-Field Deployment and Validation for Wearable Devices.Annals of biomedical engineering · 2022
    Review
  7. Article
  8. Article
  9. 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

27 authors at 4 institutions in 2 countries.

August G Domel *Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Samuel J Raymond *Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. sjray@stanford.edu.
Chiara Giordano *Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Yuzhe LiuDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Seyed Abdolmajid YousefsaniDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Michael FantonDepartment of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
Nicholas J CecchiDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Olga VovkGeneral Dynamics Information Technology, 3150 Fairview Park Drive, Falls Church, VA, 22042, USA.
Ileana PirozziDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Ali KightDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Brett AveryStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Athanasia BoumisStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Tyler FettersGeneral Dynamics Information Technology, 3150 Fairview Park Drive, Falls Church, VA, 22042, USA.
Simran JanduStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
William M MehringStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Sam MongaIntel Sports, Intel Corporation, Santa Clara, CA, 95054, USA.
Nicole MouchawarDepartment of Radiology, Stanford University, 300 Pasteur Dr, MC 5105, Stanford, CA, USA.
India RangelStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Eli RiceStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Pritha RoyStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Sohrab SamiStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Heer SinghStanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA.
Lyndia WuDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Calvin KuoDepartment of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.
Michael ZeinehDepartment of Radiology, Stanford University, 300 Pasteur Dr, MC 5105, Stanford, CA, USA.
Gerald GrantDepartment of Neurology, Stanford University, Stanford, CA, 94305, USA.
David B CamarilloDepartment of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Stanford University · USCenter for Clinical Research (United States) · USGeneral Dynamics (United States) · USIntel (United States) · US

Funding

Disseminating a validated mouthguard sensor to investigate the effect of head impacts on brain healthR24NS098518 · NINDS · STANFORD UNIVERSITY · PI CAMARILLO, DAVID, GRANT, GERALD ARTHUR · 2018 to 2021
$1.1M
NINDS NIH HHS R24 NS098518
6 · The paper itself

Abstract

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.

Indexed as

Access to InformationAlgorithmsInformation DisseminationBrain Injuries, TraumaticHumansMouth ProtectorsNeural Networks, ComputerReproducibility of ResultsSupport Vector Machine

Identifiers

PMID33820939
PMCPMC8021549
OpenAlexW3093173451

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.