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Record W4386424091 · doi:10.1097/aln.0000000000004706

A Gray Future: The Role of the Anesthesiologist in Hybrid Warfare

2023· article· en· W4386424091 on OpenAlexaff
Fredrik Granholm, Derrick Tin, Leilani Doyle, Gregory R. Ciottone

Bibliographic record

VenueAnesthesiology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineAnesthesiologyMass CasualtyIntensive carePain medicineAmerican society of anesthesiologistsMedical emergencyIntensive care medicineAnesthesia

Abstract

fetched live from OpenAlex

During the last few decades, the increasing use of asymmetric and multimodal tactics by terrorists has led anesthesiologists worldwide to analyze and discuss their role in mass casualty scenarios in more depth. Now anesthesiologists must address the new situation of hybrid threats and hybrid warfare. This will have a direct impact on anesthesiology and intensive care, and in the end, the health and well-being of critical patients of all ages. To be able to respond to a hybrid threat efficiently and effectively, it is imperative that anesthesiologists play an early and integral role in mitigation and response planning.

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 score0.367
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.342
Teacher spread0.312 · 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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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