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Record W2527871732

DETERMINATION OF INDICATORS OF EPILEPTIC SEIZURE EVENTS ON THE SEIZURE MONITORING UNIT AND THE DEVELOPMENT OF AN ELECTRONIC DATABASE

2014· article· en· W2527871732 on OpenAlexaffvenueabout
Sophie Hu

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEpilepsyMedicineDatabaseReferralDemographicsPediatricsMedical diagnosisAntiepileptic drugPsychiatryFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Epilepsy is a neurological disorder characterized by recurrent seizures and affects approximately 1% of Canadians. While treatment with medications is often effective, 40% of the 300 000 Canadians suffering from epilepsy are refractory to medications. Furthermore, only 5% of patients with refractory epilepsy respond clinically to additional antiepileptic drug (AED). This group of patients is referred to the Calgary Epilepsy Program (CEP). To clarify diagnoses, optimize therapy or for pre-surgical evaluation, they are subsequently admitted to the Seizure Monitoring Unit (SMU) for on average 8 days. However, over a fifth (21%) of SMU patients do not have seizure events while on the unit. The purpose of this study was first to develop an electronic database for SMU patients and then to utilize patient data to predict the likelihood of their seizure events on the SMU. METHODS SMU Admission/Discharge summary forms were created in REDCap, an electronic survey builder. Assessed variables include: demographics, reason for referral, seizure frequency and type, type of medications, procedures performed and tests ordered. Multiple quality of life and depression scales, including: AEP, Bacca Scale, EQ-5D-3L, GADS, GASE, NDDI-E, PANAS, PHQ-9, QOLIE-31 and TSQM-II were added for future data collection. Patient admission data (n=603) from 2008 to 2014 was analyzed using chi-squared test and Student’s t-test. Patient characteristics, including: age, number of AEDs, seizure frequency and psychotropic medications before admission were compared between those that had seizure events and those that did not have seizure events on the unit. Data was analyzed on iPython Notebook. RESULTS Patients with seizure events (n=474) had more AEDs (1.9 ± 1.1 vs 1.6 ± 1.1, p < 0.01), a higher seizure frequency (Daily vs Weekly, p < 0.05) or were less likely to be on psychotropic medications (26.5% vs 44.5%, p < 0.001) before admission. There is no statistically significant correlation between age and the occurrence of seizure events. DISCUSSION AND CONCLUSIONS CEP and SMU patient data can now be accessed through a single electronic database to facilitate epilepsy research and patient care. The database linking patient admission data to measurements of patient quality of life, depression and satisfaction will give researchers better insight into treatment outcomes for patients with epilepsy. The probability of having a seizure event on the SMU is higher with a higher number of antiepileptic drugs (p < 0.01), higher seizure frequency (p < 0.05) or lower number of psychotropic medications (p < 0.001) before admission. The use of pre-admission variables to predict the likelihood of seizure events on the SMU will help improve referral accuracy and reduce unnecessary hospitalization which costs several thousand dollars daily. Continued analysis of other variables includes seizure type, primary reason for referral, type of AED and type of psychotropic medication.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.378
Teacher spread0.329 · 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 designBench or experimental
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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Citations0
Published2014
Admission routes3
Has abstractyes

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