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Record W4319997783 · doi:10.1016/j.heliyon.2023.e12985

Smarter faster just-in-time hemorrhage control: A pilot evaluation of remotely piloted aircraft system delivered STOP-THE-BLEED equipment with just-in-time remote telementored deployment

2023· article· en· W4319997783 on OpenAlexaff
Andrew W. Kirkpatrick, Jessica McKee, John Conly, Kristin Flemons, Wade Hawkins

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsCARE CanadaSAIT PolytechnicFoothills Medical CentreUniversity of Calgary
FundersCSL Behring
KeywordsSoftware deploymentBleedMedicineRemote controlMedical emergencyTask (project management)AeronauticsEngineeringComputer scienceSurgeryComputer hardware

Abstract

fetched live from OpenAlex

Introduction: Remotely Piloted Aircraft Systems (RPAS) can access patients inaccessible to traditional rescue. Just-in-time remote telementoring (RTM) of naïve users to self-care could potentially address challenges in salvaging exsanguination in remote environments. Methods: An exsanguination self-application task was established in a wilderness location. Three volunteers-initiated distress calls to prompt RPAS precision delivered STOP-THE-BLEED kits, after which a remote mentor directed the volunteers how to self-care. Results: Limited connectivity prevented video, however each volunteer delivered images and initiated conversation with the mentor pre-RPAS arrival. Thereafter, all subjects were able to unpack and deploy hemorrhage control adjuncts under verbal direction, and to simulate self-application. All subjects were able to successfully apply wound-clamps, tourniquets, and pack wounds although one had insufficient pressure. Discussion: RPASs can deliver supplies long before human rescuers, and communication connectivity might allow remote mentoring in device application. Further development of technology and self-care paradigms for exsanguination are encouraged.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.057
GPT teacher head0.311
Teacher spread0.254 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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