Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 447

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

HEAD IMPACT SEVERITY MEASURES FOR EVALUATING MILD TRAUMATIC BRAIN INJURY RISK EXPOSURE

Open access
Greenwald, Richard M., Gwin, Joseph T., Chu, Jeffrey J., Crisco, Joseph J.·2008·Neurosurgery

Abstract

OBJECTIVE: The aims of this study were to quantify the sensitivity of various biomechanical measures (linear acceleration, rotational acceleration, impact duration, and impact location) of head impact to the clinical diagnosis of concussion in United States football players and to develop a novel measure of head impact severity combining these measures into a single score that better predicts the incidence of concussion. METHODS: On-field head impact data were collected from 449 football players at 13 organizations (n = 289,916) using in-helmet systems of six single-axis accelerometers. Concussions were diagnosed by medical staff and later associated with impact data. Principal component analysis and a weighting coefficient based on impact location were used to transform correlated head impact measures into a new composite variable, weighted principal component score (wPCS). The predictive power of linear acceleration, rotational acceleration, head injury criterion, and wPCS was quantified using receiver operating characteristic curves. The null hypothesis, that a measure was no more predictive than guessing, was tested (alpha = 0.05). In addition, receiver operating characteristic curves for wPCS and classical measures were directly compared to test the hypothesis that wPCS was more predictive of concussion than were classic measures (alpha = 0.05). RESULTS: When all of the impacts were considered, every biomechanical measure evaluated was statistically more predictive of concussion than guessing (P < 0.005). However, for the top 1 and 2% of impacts based on linear acceleration, a subset that consisted of 82% of all diagnosed concussions, only wPCS was significantly more predictive of concussion than guessing (P < 0.03); when compared with each other, wPCS was more predictive of concussion than were classical measures for the top 1 and 2% of all of the data (P < 0.04). CONCLUSION: A weighted combination of several biomechanical inputs, including impact location, is more predictive of concussion than a single biomechanical measure. This study is the first to the authors' knowledge to quantify improvements in the sensitivity of a biomechanical measure to incidence of concussion when impact location is considered.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1227/01.neu.0000318162.67472.ad

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 39 references

Cited by

See all 447

Metadata

Authors
Greenwald, R. M., Gwin, J. T., Chu, J. J., & Crisco, J. J.
Venue
Neurosurgery
Published
2008-04
Volume
62
Issue
4
Pages
789-798
ISSN
0148-396X (ISSN-L)
OpenAlex
W2120088820
Cited by
447
References
39
FWCI
14.89
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: HEAD IMPACT SEVERITY MEASURES FOR EVALUATING MILD TRAUMATIC BRAIN INJURY RISK EXPOSURE. https://app.cochat.ai/papers/10.1227/01.neu.0000318162.67472.ad
The paper
Greenwald, R. M., Gwin, J. T., Chu, J. J., & Crisco, J. J. (2008). HEAD IMPACT SEVERITY MEASURES FOR EVALUATING MILD TRAUMATIC BRAIN INJURY RISK EXPOSURE. Neurosurgery. https://doi.org/10.1227/01.neu.0000318162.67472.ad

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.