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Record W2791975369 · doi:10.1093/milmed/usx236

Biological Response to Stress During Battlefield Trauma Training: Live Tissue Versus High-Fidelity Patient Simulator

2018· article· en· W2791975369 on OpenAlexaff
Henry T. Peng, Catherine Tenn, Oshin Vartanian, Shawn G. Rhind, Jerzy Jarmasz, Homer Tien, Andrew Beckett

Bibliographic record

VenueMilitary Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsMcGill UniversityHealth Sciences CentreSunnybrook Health Science CentreDefence Research and Development Canada
Fundersnot available
KeywordsEpinephrineNeuropeptide Y receptorBattlefieldMedicineModalitiesNorepinephrineInternal medicineEndocrinologyNeuropeptidePsychologyDopamine

Abstract

fetched live from OpenAlex

INTRODUCTION: Tactical Combat Casualty Care (TCCC) training imposes psychophysiological stress on medics. It is unclear whether these stress levels vary with the training modalities selected. It is also unclear how stress levels could have an impact on medical performance and skill uptake. MATERIALS AND METHODS: We conducted a pilot study to compare the effects of live tissue (LT) with a high-fidelity patient simulator (SIM) on the level of stress elicited, performance, and skill uptake during battlefield trauma training course in an operating room (OR) and in a simulated battlefield scenario (field). In the report, we studied the effects of training modalities and their changes on stress levels by measuring different biomarkers (salivary amylase, plasma catecholamines, and neuropeptide Y) at various time points during the trauma training course. RESULTS: We found that the training resulted in significant psychophysiological stress as indicated by elevated levels of various biomarkers relative to baseline immediately after both OR and field assessment (p < 0.05). Compared with pre-OR levels, the LT training in the OR resulted in significant increases in the plasma levels of epinephrine, norepinephrine, and neuropeptide (p = 0.013, 0.023, 0.004, respectively), whereas the SIM training in the OR resulted in significant increases in the plasma levels of norepinephrine and neuropeptide (p = 0.003 and 0.008). Compared with pre-field levels, we found significant increases in plasma epinephrine concentration in the SIM group (p = 0.016), plasma norepinephrine concentration in the LT group (p = 0.015), and plasma neuropeptide Y concentration in both LT (p = 0.006) and SIM groups (p = 0.029). No differences in the changes of biomarker levels were found between LT and SIM groups in the OR and field. Compared with pre-field levels, the testing on the same modality as that in the OR in the simulated battlefield resulted in significant increases in norepinephrine and neuropeptide levels (p = 0.013 and 0.015), whereas the testing on different modalities resulted in significant increases in amylase, epinephrine, and neuropeptide levels (p = 0.016, 0.05, 0.018, respectively). There was a significantly larger increase in plasma norepinephrine concentration (p = 0.031) and a trend toward a greater increase in the salivary amylase level (p = 0.052) when the field testing involved a different modality than the OR compared with when OR and field testing involved the same modality. Although most of the biomarkers returned to baseline levels after 24 h, plasma norepinephrine levels remained significantly higher regardless of whether field testing occurred on the same or different modality compared with OR (p = 0.040 and 0.002). CONCLUSION: TCCC training led to significant increase in psychophysiological stress, as indicated by elevated levels of various biomarkers. The training modalities did not result in any differences in stress levels, whereas the switch in training modalities appeared to elicit greater stress as evidenced by changes in specific biomarkers (amylase and norepinephrine). A comparative study with a larger sample size is warranted.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.064
GPT teacher head0.331
Teacher spread0.267 · 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.

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

Citations12
Published2018
Admission routes1
Has abstractyes

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