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Record W2965091406 · doi:10.1001/jamaneurol.2019.2268

Association of Levels of Specialized Care With Risk of Premature Mortality in Patients With Epilepsy

2019· article· en· W2965091406 on OpenAlexaffabout
Mark Lowerison, Colin B. Josephson, Nathalie Jetté, Tolulope T. Sajobi, Scott B. Patten, Tyler Williamson, Rob Deardon, Herman W. Barkema, Samuel Wiebe

Bibliographic record

VenueJAMA Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of CalgaryLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsMedicineEpilepsyPediatricsRetrospective cohort studyIncidence (geometry)Psychological interventionCohortCohort studyEmergency medicineSocioeconomic statusPopulationInternal medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Importance: Patients with epilepsy are at an elevated risk of premature mortality. Interventions to reduce this risk are crucial. Objective: To determine if the level of care (non-neurologist, neurologist, or comprehensive epilepsy program) is negatively associated with the risk of premature mortality. Design, Setting, and Participants: In this retrospective open cohort study, all adult patients 18 years or older who met the administrative case definition for incident epilepsy in linked databases (Alberta Health Services administrative health data and the Comprehensive Calgary Epilepsy Programme Registry [CEP]) inclusive of the years 2002 to 2016 were followed up until death or loss to follow-up. The final analyses were performed on May 1, 2019. Exposures: Evaluation by a non-neurologist, neurologist, or epileptologist. Main Outcomes and Measures: The outcome was all-cause mortality. We used extended Cox models treating exposure to a neurologist or the CEP as time-varying covariates. Age, sex, socioeconomic deprivation, disease severity, and comorbid burden at index date were modeled as fixed-time coefficients. Results: A total 23 653 incident cases were identified (annual incidence of 89 per 100 000); the mean age (SD) at index date was 50.8 (19.1) years and 12 158 (50.3%) were women. A total of 14 099 (60%) were not exposed to specialist neurological care, 9554 (40%) received care by a neurologist, and 2054 (9%) received care in the CEP. In total, 4098 deaths (71%) occurred in the nonspecialist setting, 1481 (26%) for those seen by a neurologist, and 176 (3%) for those receiving CEP care. The standardized mortality rate was 7.2% for the entire cohort, 9.4% for those receiving nonspecialist care, 5.6% for those seen by a neurologist, and 2.8% for those seen in the CEP. The hazard ratio (HR) of mortality was lower in those receiving neurologist (HR, 0.85; 95% CI, 0.77-0.93) and CEP (HR, 0.49; 95% CI, 0.38-0.62) care. In multivariable modeling, specialist care, the age at index, and disease severity were retained in the final model of the association between specialist care and mortality. Conclusions and Relevance: Exposure to specialist care is associated with incremental reductions in the hazard of premature mortality. Those referred to a comprehensive epilepsy program received the greatest benefit.

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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.259
Teacher spread0.250 · 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".

Quick stats

Citations63
Published2019
Admission routes2
Has abstractyes

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