Evidence map›Paper›PMID 42814292›Full record

ArticleAnnals of biomedical engineering2026

Relating Real-World Falls to Laboratory Test Conditions.

Susanna M Gagliardi, Nicole E-P Stark, Michael L Madigan, Steve Rowson

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

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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

4 authors.

Susanna M GagliardiDepartment of Biomedical Engineering, Virginia Tech, Blacksburg, USA.
Nicole E-P StarkDepartment of Biomedical Engineering, Virginia Tech, Blacksburg, USA. nestark@vt.edu.ORCID http://orcid.org/0000-0003-3186-1885
Michael L MadiganDepartment of Industrial and Systems Engineering, Virginia Tech, Blacksburg, USA.
Steve RowsonDepartment of Biomedical Engineering, Virginia Tech, Blacksburg, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeFalls account for over half of fatal work-related traumatic brain injuries, yet current safety helmet standards do not test energy levels representative of falls. Additionally, little is known about how fall impact mitigating mechanisms (FIMMs), such as fall arrest movements and/or object contact, and reduce head impact velocity. This study used a multi-step analytical approach to estimate head impact speeds from various fall heights while factoring in energy reduction due to FIMMs.

methodsLaboratory drop tests established peak linear accelerations (PLAs) and head injury criterion (HIC) values across a range of impact velocities. These PLA/HIC values were then input into skull fracture risk curves to establish the risk of skull fracture at various head impact speeds. At matched levels of risk, a risk curve relating real-world fall height to skull fracture risk was used to estimate the fall heights associated with different head impact velocities. A worst-case head impact velocity representing a direct, head-first fall without FIMMs was calculated using basic energy assumptions from the specified fall height. FIMMs were quantified as the percent reduction in the worst-case velocity from a given fall height to the laboratory head impact velocity.

resultsFIMMs may reduce head impact velocity by 44.5-64.4% compared to a head-first impact without FIMMs.

conclusionTo evaluate safety helmets under realistic impact conditions, lab-based head impact velocities should be reduced by 44.5-64.4% from the worst-case velocity to account for a population-level estimate energy reduction associated with FIMMs.

Indexed as

ConstructionFallsHelmet testingInjury risk curves

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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.