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Record W2123076651 · doi:10.1017/s0317167100014530

Global Incidence and Prevalence of Traumatic Spinal Cord Injury

2013· review· en· W2123076651 on OpenAlexafffundvenue
Julio C. Furlan, Brodie M. Sakakibara, William C. Miller, Andrei V. Krassioukov

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsGF Strong Rehabilitation CentreToronto Rehabilitation InstituteUniversity Health NetworkUniversity of TorontoInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersOntario Neurotrauma FoundationRick Hansen Institute
KeywordsIncidence (geometry)MedicinePrevalenceSpinal cord injuryDemographyEpidemiologyEnvironmental healthSpinal cordPathologyPsychiatry

Abstract

fetched live from OpenAlex

This systematic review examines the incidence and prevalence of traumatic spinal cord injury (SCI) in different countries worldwide and their trends over time. The literature search of the studies published between 1950 and 2012 captured 1,871 articles of which 64 articles on incidence and 13 articles on prevalence fulfilled the inclusion and exclusion criteria. The global incidence of SCI varied from 8.0 to 246.0 cases per million inhabitants per year. The global prevalence varied from 236.0 to 1,298.0 per million inhabitants. In addition to regional differences regarding the prevalence rates of SCI across the globe, there has been a trend towards increasing prevalence rates over the last decades. Our results suggest a relatively broad variation of incidence and prevalence rates of SCI among distinctive geographic regions. These results emphasize the need for further studies on incidence and prevalence of SCI, and for international standards and guidelines for reporting on SCI.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0130.016
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.421
Teacher spread0.284 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations299
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicSpinal Cord Injury ResearchFrench-language works237,207