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Record W2593525832 · doi:10.1001/jamaneurol.2016.5042

Association of Depression and Treated Depression With Epilepsy and Seizure Outcomes

2017· article· en· W2593525832 on OpenAlexaffabout
Colin B. Josephson, Mark Lowerison, Isabelle A. Vallerand, Tolulope T. Sajobi, Scott B. Patten, Nathalie Jetté, Samuel Wiebe

Bibliographic record

VenueJAMA Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsEpilepsyInterquartile rangeDepression (economics)MedicineHazard ratioCohortOdds ratioCohort studyPopulationPsychiatryInternal medicinePediatricsConfidence intervalEnvironmental health

Abstract

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Importance: A bidirectional relationship exists between epilepsy and depression. However, any putative biological gradient between depression severity and the risk of epilepsy, and the degree to which depression mediates the influence of independent risk factors for epilepsy, has yet to be examined. Objective: To determine the effect of depression on the risk of epilepsy and seizure outcomes. Design, Setting, and Participants: An observational study of a population-based primary care cohort (all patients free of prevalent depression and epilepsy at 18-90 years of age who were active after the Acceptable Mortality Reporting date in The Health Improvement Network database) and a prospectively collected tertiary care cohort (all patients whose data were prospectively collected from the Calgary Comprehensive Epilepsy Programme). The analyses were performed on March 16, 2016. Main Outcome and Measures: The hazard of developing epilepsy after incident depression and vice versa was calculated. In addition, a mediation analysis of the effect of depression on risk factors for epilepsy and the odds of seizure freedom stratified by the presence of depression were performed. Results: We identified 10 595 709 patients in The Health Improvement Network of whom 229 164 (2.2%) developed depression and 97 177 (0.9%) developed epilepsy. The median age was 44 years (interquartile range, 32-58 years) for those with depression and 56 years (interquartile range, 43-71 years) for those with epilepsy. Significantly more patients with depression (144 373 [63%] were women, and 84 791 [37%] were men; P < .001) or epilepsy (54 419 [56%] were women, and 42 758 [44%] were men; P < .001) were female. Incident epilepsy was associated with an increased hazard of developing depression (hazard ratio [HR], 2.04 [95% CI, 1.97-2.09]; P < .001), and incident depression was associated with an increased hazard of developing epilepsy (HR, 2.55 [95% CI, 2.49-2.60]; P < .001) There was an incremental hazard according to depression treatment type with lowest risk for those receiving counselling alone (HR, 1.84 [95% CI, 1.30-2.59]; P < .001), an intermediate risk for those receiving antidepressants alone (HR, 3.43 [95% CI, 3.37-3.47]; P < .001), and the highest risk for those receiving both (HR, 9.85 [95% CI, 5.74-16.90]; P < .001). Furthermore, depression mediated the relationship between sex, social deprivation, and Charlson Comorbidity Index with incident epilepsy, accounting for 4.6%, 7.1%, and 20.6% of the total effects of these explanatory variables, respectively. In the Comprehensive Epilepsy Programme, the odds of failing to achieve 1-year seizure freedom were significantly higher for those with depression or treated depression. Conclusions and Relevance: Common underlying pathophysiological mechanisms may explain the risk of developing epilepsy following incident depression. Treated depression is associated with worse epilepsy outcomes, suggesting that this may be a surrogate for more severe depression and that severity of depression is associated with severity of epilepsy.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.292
Teacher spread0.280 · 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 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".

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Citations193
Published2017
Admission routes2
Has abstractyes

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