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Simultaneous Fracture of Every Cervical Vertebra

2002· article· en· W2010626963 on OpenAlexaff
Merv Letts, Darin Davidson, David Healey

Bibliographic record

VenueSpine · 2002
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineVertebraCervical spineSurgeryTraction (geology)Reduction (mathematics)

Abstract

fetched live from OpenAlex

STUDY DESIGN: The case of a 14-year-old boy who sustained simultaneous fractures of every cervical vertebra in a high-energy snowmobile accident is reported. OBJECTIVE: To describe a case of multiple cervical spine fractures and their management. SUMMARY OF BACKGROUND DATA: Injuries from all-terrain vehicles and off-road vehicles, including snowmobiles, are increasing in severity and frequency. The reported case illustrates a result of high-impact loading in which the driver struck his head after being thrown from a snowmobile at high speed. METHODS: The 14-year-old boy in the reported case fractured C1-C7, but had no neurologic sequelae. RESULTS: The fractures were treated with a halo vest after traction and reduction of the displaced odontoid fracture. All the fractures healed with no residual cervical instability. CONCLUSIONS: This case report is the first to describe a patient of any age who sustained simultaneous fractures of every cervical vertebra. Treatment with a halo vest was successful in protecting the cervical spine until healing was complete.

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 score0.021

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 studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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

Citations6
Published2002
Admission routes1
Has abstractyes

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