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Record W4376637285 · doi:10.1093/milmed/usac406

Comparison of In-service Reduced vs. Full Torso Coverage Armor for Females

2023· article· en· W4376637285 on OpenAlexafffundabout
Rebecca Wendland, Linda Bossi, Edward T. Nakaza, Michele Oliver

Bibliographic record

VenueMilitary Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsDefence Research and Development CanadaUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMitacsDefence Research and Development Canada
KeywordsTorsoMedicineBiomechanicsKinematicsVESTPhysical medicine and rehabilitationPhysical therapySimulationAnatomyEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION: Body armor and torso-borne equipment are critical to the survivability and operational effectiveness of a soldier. Historically, in-service designs have been predominantly designed for males or unisex, which may be disadvantageous for females who are shaped differently and, on average, smaller in stature and mass than their male counterparts. This study assesses the biomechanical and performance impact of two Canadian in-service armors and fighting load conditions on females. MATERIALS AND METHODS: Four tasks (i.e., range of motion, treadmill march [×2], and a wall obstacle) were performed in a Baseline condition and two in-service torso-borne equipment conditions; the full torso coverage (FTC) condition has full upper torso soft armor with the fighting load carried in a separate vest, while the reduced coverage (RC) has a plate carrier with fighting load integrated into the armor carrier, bulk positioned higher, and less torso coverage. Both used identical combat loads and front and back armor plates. Trunk range of motion, march lower limb kinematics, march shoulder and hip skin pressures, perceived discomfort after the march, and time to traverse a wall obstacle were captured. Data were collected to assess the biomechanics and usability of the systems for eight females, representative of military recruits. Linear mixed-effects models were created, and analysis of variances (ANOVAs) were then performed on all the outcome measures (P < .05). Tukey's post-hoc procedures were performed when appropriate (P < .05). RESULTS: There were significant differences between the RC and FTC for the sit and reach test (P < .001), lateral bend test (P < .001), and wall traverse time (P < .01). In all cases, the RC outperformed FTC. There were no differences between the two in-service conditions with respect to hip, knee, and ankle flexion/extension. The RC average skin pressure was higher than the FTC at the left and right shoulders by 103% and 79%, respectively, and peak skin pressure at the left shoulder by 75%. Both in-service conditions showed decrements in performance from Baseline for sit and reach (P < .001), lateral bend (P < .001), and peak hip and knee flexion (P < .01) with the FTC showing decreases in trunk rotation (P < .001) and wall traverse time (P < .01). CONCLUSIONS: Improved outcomes for the RC can be attributed to design differences. The lower placement of bulk in FTC may act as a physical barrier during range of motion tasks and the wall obstacle. The presence of shoulder caps on FTC provides another physical barrier that likely impedes full movement through the arms and shoulders. While the narrower shoulder straps of the RC remove the barrier, it causes more concentrated skin pressures on the shoulder that can lead to injury. The results suggest that the RC offers a potential for increased operational effectiveness in females (and potentially for males) compared to the FTC system. Shoulder pressure, an important predictor of discomfort and injury, is the only measure for which FTC outperformed the RC. Future torso-borne equipment designs targeting this outcome measure could help increase the effectiveness of the RC and other similar systems that reduce torso coverage, though survivability implications must also be considered.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.229
GPT teacher head0.526
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2023
Admission routes3
Has abstractyes

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