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Spinal cord injury and type 2 diabetes

2013· article· en· W1998631080 on OpenAlexafffundabout
Jacquelyn J. Cragg, Vanessa K. Noonan, Marcel F. Dvorak, Andrei V. Krassioukov, G.B. John Mancini, Jaimie Borisoff

Bibliographic record

VenueNeurology · 2013
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesBritish Columbia Institute of TechnologyPraxis Spinal Cord Institute
FundersCanadian Institutes of Health Research
KeywordsOdds ratioMedicineConfidence intervalType 2 diabetesBody mass indexLogistic regressionConfoundingNational Health and Nutrition Examination SurveyDiabetes mellitusCross-sectional studyDemographyOddsInternal medicineEnvironmental healthPopulationEndocrinologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to evaluate the association between spinal cord injury (SCI) and type 2 diabetes in a large representative sample and to determine whether an association exists irrespective of known risk factors for type 2 diabetes. METHODS: Data were obtained on 60,678 respondents to the Statistics Canada 2010 Cycle of the cross-sectional Canadian Community Health Survey. Multivariable logistic regression, incorporating adjustment for confounders and probability weights to account for the Canadian Community Health Survey sampling method, was conducted to quantify this association. RESULTS: After adjustment for both sex and age, SCI was associated with a significant increased odds of type 2 diabetes (adjusted odds ratio = 1.66, 95% confidence interval 1.16-2.36). These heightened odds persisted after additional adjustment for smoking status, hypertension status, body mass index, daily physical activity, alcohol intake, and daily consumption of fruits and vegetables (fully adjusted odds ratio = 2.45, 95% confidence interval 1.34-4.47). CONCLUSIONS: There is a strong association between SCI and type 2 diabetes, which is not explained by known risk factors for type 2 diabetes.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.327
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.039
GPT teacher head0.366
Teacher spread0.328 · 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

Citations136
Published2013
Admission routes3
Has abstractyes

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