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Record W2103174345 · doi:10.1136/ip.2010.029215.688

The epidemiology and clinical features of multiple, non-contiguous spine injuries in children

2010· article· en· W2103174345 on OpenAlexaff
Paul J. Moroz, Stephen Kingwell

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineIncidence (geometry)EpidemiologyInjury preventionRetrospective cohort studyPoison controlSpinal injuryPediatricsSurgeryEmergency medicineInternal medicineSpinal cord injurySpinal cord

Abstract

fetched live from OpenAlex

Multiple – non-contiguous spinal injuries (NCSI) are complex injuries frequently missed and with increased potential for adverse outcomes. Adult incidence is reported as 1.6–34%, with greater risk of mortality and fracture instability; the paediatric incidence is unknown. A retrospective review at an academic paediatric trauma centre over a 15 year period identified 25 (11.8%) out of 211 patients with NCSI, with a mean age of 10.7 years. MVC was the primary cause for ages 0–9, while a fall was seen for ages 10–17. The mean number of vertebral levels injured was 3.2. The most common region was the thoracic spine, with a mean of 5.4 (range 1–22) intact vertebral levels between injuries. Twenty-four percent with NCSI had a neurologic deficit versus 9.7% with single level, contiguous injuries (RR 2.48 (1.09, 5.65)). Seven (78%) of nine patients aged 0–9 suffered an associated injury, usually a visceral injury. Mortality was 8.0% in patients with NCSI versus 2.7% in patients with single level or contiguous injury. Children with NCSI have a higher mortality and suffer more serious neurologic injury than with single level injury. Injured spinal segments occur at a greater distance in patients with neurologic injury or death. Younger patients with NCSIs had a high rate of associated injuries. Clinicians must be aware of the incidence of NCSIs in children, as well as their associations.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.017
GPT teacher head0.376
Teacher spread0.359 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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