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Record W3013875364 · doi:10.1097/scs.0000000000006321

The Face of War: Maxillofacial Patients in the Syrian Civil War

2020· article· en· W3013875364 on OpenAlexaff
Yasmine Ghantous, Hany Bahouth, Adi Rachmiel, Murad Abdelraziq, Michael V. Joachim, I. Abu-El-Naaj

Bibliographic record

VenueJournal of Craniofacial Surgery · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversité de MontréalMontreal General Hospital
Fundersnot available
KeywordsMedicineSoft tissueDepression (economics)RifleReduction (mathematics)Injury preventionBattlePoison controlSurgeryDentistryGeneral surgeryMedical emergency

Abstract

fetched live from OpenAlex

The type of the armed conflict on the Syrian battle field acquired several types of injuries; including injuries that were caused by explosive, shrapnel and blast injuries.In the current study, the authors conducted an overview of maxillofacial patients, who mainly suffered from ballistic injuries in term of injuries, reconstruction, and management.Overall, 53 maxillofacial Syrian patients were treated. The most prominent injury was soft tissue lacerations (21/97) and in terms of hard tissue injuries, the most prominent site was the mandible (N = 19) while the ramus and the body presented the most common sub-sites of injury. Hard tissue injuries were treated either by close or open reduction to obtain primary stabilization.From the psychological aspect, most of the patients suffered from guilt for leaving the combat area, those patients were mostly males in their 20s or 30s. On the other hand, older patients suffered mainly from depression, stress, and fear of returning to their home land.To conclude, the Syrian civil war has several characteristics that defer from other combats. Thus, the management of Syrian patients has to be tailored accordingly.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.054
GPT teacher head0.340
Teacher spread0.286 · 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 designNot applicable
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

Citations9
Published2020
Admission routes1
Has abstractyes

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