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Record W3199962847 · doi:10.54029/2021zsu

A collaborative effort to build an epilepsy program in central Vietnam

2021· article· en· W3199962847 on OpenAlexaff
Thi Phuoc Yen Tran, Van Tri Truong, Dinh Toan Nguyen, Duy Nguyen, Alain Bouthillier, Dang Khoa Nguyen

Bibliographic record

VenueNeurology Asia · 2021
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsEpilepsyIntractable epilepsyPopulationMedicineDeveloping countryBusinessEconomic growthEnvironmental healthPsychiatryEconomics

Abstract

fetched live from OpenAlex

The Socialist Republic of Vietnam is a developing country with an estimated population of 96.2 million as of 2019. The prevalence rate of epilepsy is between 4.4 and 14 per 1000 population. The care of epileptic patients in Vietnam is suboptimal for many reasons: insufficient resources, poor infrastructure, lack of education (to both patients and physicians), and inadequate offer of medical and surgical treatment options for 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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.012
GPT teacher head0.328
Teacher spread0.316 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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