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Record W2921642007 · doi:10.1097/scs.0000000000005186

Transcranial Blade Injuries and Principles of Their Safe Extraction

2019· article· en· W2921642007 on OpenAlexaff
Alain J. Azzi, Rajeet Singh Saluja, Peter Mankowski, Susan M. Wakil, Bryan Arthurs, Lucie Lessard

Bibliographic record

VenueJournal of Craniofacial Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineOrbit (dynamics)Dissection (medical)SkullSurgeryCraniofacialRadiological weaponSoft tissueRadiology

Abstract

fetched live from OpenAlex

Retained cranial blade injuries are uncommon events lacking standardized recommendations for appropriate surgical extraction. The authors present a case of a 30-year-old male who sustained a penetrating blade injury of the left orbit with intracranial extension through the skull base into the temporal lobe. The patient walked to the emergency room and remained alert. Clinically, the patient had only a small laceration of the left upper eyelid with no gross visual impairment.The radiological investigation confirmed the presence of a knife blade in the orbit. Intraoperative management included an intracranial approach and an extracranial craniofacial dissection for blade visualization and soft tissue protection, globe protection and to avoid any major bleeding. A thorough review of the penetrating cranial injuries literature is presented and a trauma management algorithm is offered for the care of similar injuries.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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