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Record W3096287217 · doi:10.1093/milmed/usaa332

Neuropsychological, Neurocognitive, Vestibular, and Neuroimaging Correlates of Exposure to Repetitive Low-Level Blast Waves: Evidence From Four Nonoverlapping Samples of Canadian Breachers

2020· article· en· W3096287217 on OpenAlexafffundabout
Oshin Vartanian, Lori Coady, Kristen Blackler, Brenda Fraser, Bob Cheung

Bibliographic record

VenueMilitary Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsDepartment of National DefenceUniversity of TorontoDefence Research and Development Canada
FundersMinistère de la Défense Nationale
KeywordsNeurocognitivePhysical medicine and rehabilitationNeuropsychologyConcussionContext (archaeology)Rivermead post-concussion symptoms questionnaireNeuroimagingMedicinePopulationAudiologyBalance (ability)PsychologyPhysical therapyPoison controlInjury preventionRehabilitationCognitionPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: We assessed the utility of a battery of neuropsychological, neurocognitive, physiological (balance, ataxia, postural tremor), and neuroimaging measures for studying the effects of blast waves in breachers-a population repeatedly exposed to low-level blast during military training and operations. MATERIALS AND METHODS: Data were collected from four nonoverlapping samples, in the course of similarly structured 4-day breacher training exercises in successive years involving a combination of indoor and outdoor blast events. In all cases, self-report and neuropsychological measures were administered once at baseline (i.e., 1 day before the start of training). In years 1-2, neurocognitive and physiological measures were administered daily before and after training. In years 3-4, neurocognitive data were collected once at baseline. In Year 4, we introduced 3 modifications to our design. First, in addition to breachers, we also collected data from sex-and age-matched military controls at the same time points. Second, we assessed balance, ataxia, and postural tremor immediately following blast exposure "in the field," enabling us to quantify its acute effects. Third, structural magnetic resonance imaging (MRI) scans were acquired before and after the 4-day training exercise to explore differences between breachers and controls at baseline, as well as possible training-related changes using voxel-based morphometry. These design modifications were made to enable us to test additional hypotheses in the context of the same training exercise. RESULTS: At baseline, scores on the "Rivermead Post Concussion Symptoms Questionnaire," "RAND SF-36" (physical functioning, role limitation due to physical health, social functioning, energy/fatigue, general health), and "Short Musculoskeletal Function Questionnaire" distinguished breachers from controls. Also at baseline, the MRI data revealed that there was greater regional gray matter volume in controls compared to breachers in the right superior frontal gyrus. Balance, ataxia, and postural tremor did not exhibit sensitivity to the acute effects of blast in the field, nor did neurocognitive measures to its cumulative or daily effects. CONCLUSION: Our exploratory results suggest that self-report neuropsychological measures and structural MRI hold promise as sensitive measures for quantifying the long-term, cumulative effects of blast exposure in breachers. We discuss the limitations of our study and the need for prospective longitudinal data for drawing causal inferences regarding the impact of blast exposure on breachers' health and performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.078
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.207
GPT teacher head0.328
Teacher spread0.121 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations29
Published2020
Admission routes3
Has abstractyes

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