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Record W2258646421 · doi:10.7205/milmed-d-10-00113

Interdisciplinary Approach to the Management of an 18-Month-Old Burn Victim in Afghanistan

2011· article· en· W2258646421 on OpenAlexaff
Darryl Tong, Vincent Trottier

Bibliographic record

VenueMilitary Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsCanadian Armed Forces
FundersBadan Riset dan Inovasi NasionalSmith and Nephew
KeywordsSoftware deploymentMultinational corporationUnit (ring theory)Military medicineFace (sociological concept)Medical emergencyMedicineMedical unitPolitical scienceSurgeryEngineeringPsychologySociologyLaw

Abstract

fetched live from OpenAlex

An 18-month-old boy with partial and full thickness burns to his upper face and scalp was treated by an interdisciplinary team at the Role 3 Multinational Medical Unit in Kandahar. A novel approach to his burn management is presented, highlighting some of the issues and constraints that military surgeons are faced with while on operational deployment in a theater of conflict.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.094
GPT teacher head0.400
Teacher spread0.306 · 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 designCase report
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

Citations3
Published2011
Admission routes1
Has abstractyes

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