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Record W4404256328 · doi:10.1097/scs.0000000000010802

A Mobile Craniofacial Surgery Unit: Reconstructing Casualties of War in Ukraine

2024· article· en· W4404256328 on OpenAlexaffabout
Kira Antonyshyn, Tara Lynn Teshima, Sultan Al‐Shaqsi, Danny Enepekides, Kevin Higgins, Carolyn Lévis, Leif Sigurdson, John H. Phillips, Oleh Antonyshyn

Bibliographic record

VenueJournal of Craniofacial Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsUniversity of ManitobaWinnipeg Regional Health AuthorityHospital for Sick ChildrenSt. Joseph’s Healthcare HamiltonHealth Sciences CentreMcMaster UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineCraniofacialCraniofacial surgeryUnit (ring theory)General surgeryMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

This paper describes the development and implementation of a mobile craniofacial surgical unit designed to address complex posttraumatic craniofacial deformities in both civilian and military casualties resulting from Russia's invasion of Ukraine. Restricted air space, limited possibilities for transportation of personnel and equipment, frequent interruption of power and water supply, and constant threat of injury to patients and medical personnel from missile and drone strikes, precludes reliable and safe delivery of tertiary care. The Canada Ukraine Surgical Aid Program (CUSAP) addressed these challenges by establishing a mobile craniofacial surgery unit, operating just outside of the war zone. The following report characterizes the civilian and military casualties, highlights the barriers to the provision of adequate tertiary care locally, and provides a detailed description of the measures that were taken to organize the mobile unit. The effectiveness of this program is documented, and specific challenges are illustrated through case examples. We believe this model serves as a template for delivering surgical aid to victims of any global disaster where care cannot be provided locally.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.302
Teacher spread0.263 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

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