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2008· article· en· W4239211476 on OpenAlexaboutno aff

Bibliographic record

VenueEpilepsia · 2008
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsComorbidityEpilepsyMedicinePopulationPsychiatryEmergency departmentFamily medicinePediatricsEnvironmental health

Abstract

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Churlsu Kwon*, A. Metcalfe†,‡, Mingfu Liu‡, H. Quan‡, S. Wiebe†,‡ and Nathalie Jette†,‡*University College of London, London, United Kingdom; †Clinical Neurosciences, University of Calgary, Calgary, AB, Canada and ‡Community Health Sciences, University of Calgary, Calgary, AB, Canada Rationale: Defining comorbidity associated with epilepsy is necessary in order to adequately manage this patient population, and to ensure proper resources are in place for these individuals. There are no population-based studies addressing both somatic and psychiatric comorbidity pre and post epilepsy diagnosis to address the following questions: (1) Is the prevalence of comorbidity higher in those with epilepsy compared to those without epilepsy before the epilepsy diagnosis and (2) Does the prevalence of comorbidity increase in those with epilepsy after their epilepsy diagnosis? The objective of this study was to determine the prevalence of comorbidity in those with and without epilepsy in the two years before diagnosis and in the year after diagnosis. Somatic and Psychiatric Comorbidity Pre and Post Epilepsy Diagnosis. 95% CI – 95 percent confidence interval. Only the comorbidity which were significantly increased post epilepsy diagnosis are shown above. Methods: Data was obtained on 26,235 individuals from the following linked administrative databases between the years 1996/1997 to 2003/2004: a provincial health care insurance plan registry, a hospital discharge abstract database, an emergency room visits database and a physician claims database in a large Canadian health region. A case was defined as anyone who had 2 physician claims or 1 hospitalization or 1 emergency room visit in two years for epilepsy. Four-to-one matching was used, and controls were matched on age and sex. Results: Our sample consisted of 5,247 subjects with epilepsy and 20,988 subjects without epilepsy, with a mean age of 37.4 ± 22.6 years (S.D.) (range 0.01–96.4 years). In the two years prior to epilepsy diagnosis and in the year following diagnosis there was a statistically significant higher rate of all comorbidity studied (heart disease, peripheral vascular disorders, chronic pulmonary disease, renal failure, liver disease, diabetes, peptic ulcer disease excluding bleeding, AIDS/HIV, cancer, CNS tumor, rheumatoid arthritis/collagen vascular disease, stroke, pneumonia, dementia, hypertension, traumatic brain injury, multiple sclerosis, cerebral palsy, anoxic brain injury, encephalopathy, alcohol abuse, drug abuse, psychoses, depression, fractures, and Crohn's disease/colitis). The relative risk (RR) of having any comorbidity in epilepsy was 1.77 (95% CI 1.72–1.81) pre-diagnosis and 2.11 (95% CI 2.04–2.18) post diagnosis. The following comorbidity were significantly more prevalent in the one year post epilepsy diagnosis than in the two years preceding diagnosis (see Table): heart disease, cancer (excluding brain tumors), dementia, drug abuse, depression, pneumonia, and fractures. Conclusions: This study indicates that epilepsy is associated with a higher likelihood of having comorbidity pre- and post-epilepsy diagnosis, and that the prevalence of comorbidity increases after the diagnosis of epilepsy. The temporal association does not imply causation, but raises important questions in this regards. As this population is likely to have more contact with the health care system to manage their various conditions, there should be more opportunities to emphasize the prevention of the development of new comorbidity.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.199
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8010.678

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 head0.048
GPT teacher head0.318
Teacher spread0.270 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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