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Record W7066354483

Global prevalence and incidence of traumatic spinal cord injury

2014· article· en· W7066354483 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Cartography
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Systematic reviewMEDLINESpinal cord injuryPoison controlInjury preventionPrevalenceOccupational safety and health
DOInot available

Abstract

fetched live from OpenAlex

Anoushka Singh*, Lindsay Tetreault*, Suhkvinder Kalsi-Ryan, Aria Nouri, Michael G FehlingsToronto Western Research Institute, Toronto Western Hospital, Toronto, Ontario, Canada  *These authors contributed equally to this paper Background: Spinal cord injury (SCI) is a traumatic event that impacts a patient's physical, psychological, and social well-being and places substantial financial burden on health care systems. To determine the true impact of SCI, this systematic review aims to summarize literature reporting on either the incidence or prevalence of SCI. Methods: A systematic search was conducted using PubMed, MEDLINE, MEDLINE in process, EMBASE, Cochrane Controlled Trial Register, and Cochrane Database of Systematic Reviews to identify relevant literature published through June 2013. We sought studies that provided regional, provincial/state, or national data on the incidence of SCI or reported estimates of disease prevalence. The level of evidence of each study was rated using a scale that evaluated study design, methodology, sampling bias, and precision of estimates. Results: The initial search yielded 5,874 articles, 48 of which met the inclusion criteria. Forty-four studies estimated the incidence of SCI and nine reported the prevalence, with five discussing both. Of the incidence studies, 14 provided figures at a regional, ten at a state or provincial level and 21 at a national level. The prevalence of SCI was highest in the United States of America (906 per million) and lowest in the Rhone-Alpes region, France (250 per million) and Helsinki, Finland (280 per million). With respect to states and provinces in North America, the crude annual incidence of SCI was highest in Alaska (83 per million) and Mississippi (77 per million) and lowest in Alabama (29.4 per million), despite a large percentage of violence injuries (21.2%). Annual incidences were above 50 per million in the Hualien County in Taiwan (56.1 per million), the central Portugal region (58 per million), and Olmsted County in Minnesota (54.8 per million) and were lower than 20 per million in Taipei, Taiwan (14.6 per million), the Rhone-Alpes region in France (12.7 per million), Aragon, Spain (12.1 per million), Southeast Turkey (16.9 per million), and Stockholm, Sweden (19.5 per million). The highest national incidence was 49.1 per million in New Zealand, and the lowest incidences were in Fiji (10.0 per million) and Spain (8.0 per million). The majority of studies showed a high male-to-female ratio and an age of peak incidence of younger than 30 years old. Traffic accidents were typically the most common cause of SCI, followed by falls in the elderly population. Conclusion: This review demonstrates that the incidence, prevalence, and causation of SCI differs between developing and developed countries and suggests that management and preventative strategies need to be tailored to regional trends. The rising aging population in westernized countries also indicates that traumatic SCI secondary to falls may become an increasing public health challenge and that incidence among the elderly may rise with increasing life expectancy. Keywords: SCI, causation, epidemiology

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.155
GPT teacher head0.539
Teacher spread0.384 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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