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Record W3095458223 · doi:10.1111/ane.13368

Eslicarbazepine acetate response in intellectual disability population versus general population

2020· article· en· W3095458223 on OpenAlexaboutno aff
Jon Allard, Charlotte Lawthom, William Henley, Brendan McLean, Sharon Hudson, Phil Tittensor, Sanjeev Rajakulendran, Shan Ellawela, Adrian Pace, Rohit Shankar

Bibliographic record

VenueActa Neurologica Scandinavica · 2020
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual disabilityEpilepsyPopulationMedicineWelshQuarter (Canadian coin)PediatricsPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: A quarter of people with intellectual disability (ID) have epilepsy, compared to approximately one in a hundred across the general population. Evidence for the safe and effective prescribing of antiepileptic drugs (AEDs) for those with ID is, however, limited. AIMS OF STUDY: This study seeks to strengthen the research evidence around Eslicarbazepine Acetate (ESL), a new AED, by comparing response of individuals with ID to those from the general population who do not have ID. METHODS: A single data set was created through retrospective data collection from English and Welsh NHS Trusts. The UK-based Epilepsy Database Research Register (Ep-ID) data collection and analysis method were used. RESULTS: Data were collected for 93 people (36 ID and 57 'no ID'). Seizure improvement of '>50%' was higher at 12 months for 'no ID' participants (56%), compared to ID participants (35%). Retention rates were slightly higher for those with ID (56% compared to 53%). Neither difference was significant. CONCLUSIONS: Tolerance and Efficacy for ID and 'no ID' people in our data set were similar. Seizure improvement and retention rates were slightly lower than that found in other European data sets, but findings strengthen the evidence for the use of ESL in the ID population.

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)0.004
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 score0.085
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.333
Teacher spread0.276 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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