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Cross‐Country Measures for Monitoring Epilepsy Care

2007· article· en· W2108327607 on OpenAlexaff
Charles E. Begley, Gus A. Baker, Ettore Beghi, James Butler, Daniel Chisholm, John T. Langfitt, Pierre Lévy, Christoph Pachlatko, Samuel Wiebe, Karen Donaldson

Bibliographic record

VenueEpilepsia · 2007
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsFoothills Medical Centre
FundersEpilepsy Action
KeywordsEpilepsyHealth careCommissionDeveloping countryBusinessMedicineActuarial scienceEconomicsPsychiatryFinanceEconomic growth

Abstract

fetched live from OpenAlex

PURPOSE: The International League Against Epilepsy (ILAE) Commission on Healthcare Policy in consultation with the World Health Organization (WHO) examined the applicability and usefulness of various measures for monitoring epilepsy healthcare services and systems across countries. The goal is to provide planners and policymakers with tools to analyze the impact of healthcare services and systems and evaluate efforts to improve performance. METHODS: Commission members conducted a systematic literature review and consulted with experts to assess the nature, strengths, and limitations of the treatment gap and resource availability measures that are currently used to assess the adequacy of epilepsy care. We also conducted a pilot study to determine the feasibility and applicability of using new measures to assess epilepsy care developed by the WHO including Disability-Adjusted Life Years (DALYs), responsiveness, and financial fairness. RESULTS: The existing measures that are frequently used to assess the adequacy of epilepsy care focus on structural or process factors whose relationship to outcomes are indirect and may vary across regions. The WHO measures are conceptually superior because of their breadth and connection to articulated and agreed upon outcomes for health systems. However, the WHO measures require data that are not readily available in developing countries and most developed countries as well. CONCLUSION: The epilepsy field should consider adopting the WHO measures in country assessments of epilepsy burden and healthcare performance whenever data permit. Efforts should be made to develop the data elements to estimate the measures.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.062
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.365
Teacher spread0.333 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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