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Record W2090189145 · doi:10.1517/14656560903140525

The economic implications of generic substitution of antiepileptic drugs: a review of recent evidence

2009· review· en· W2090189145 on OpenAlexaffabout
Mei Sheng Duh, Kevin E. Cahill, Pierre Emmanuel Paradis, Pierre Y. Crémieux, Paul E. Greenberg

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2009
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineFood and drug administrationBioequivalenceGeneric drugMEDLINEDrugSubstitution (logic)Drug approvalAntiepileptic drugAlternative medicineEpilepsyFamily medicinePsychiatryPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: The US Food and Drug Administration (FDA) considers generic and branded drugs to be therapeutically equivalent if they are pharmaceutically equivalent and bioequivalent. The American Academy of Neurology (AAN) disagrees and opposes generic substitution of branded antiepileptic drugs (AEDs) without physician and patient approval due to the risk of loss of seizure control. OBJECTIVE: To review the evidence to date surrounding the economic impact of brand-to-generic substitutions of AEDs. METHODS: A systematic search of PubMed and MEDLINE was conducted; the bibliographies of key articles obtained from the search were used to identify additional sources. RESULTS/CONCLUSION: Current literature suggests statistically higher overall healthcare costs during periods of generic AED use than during periods when branded AED are used, consistently demonstrated across different countries (Canada and the USA) and in both stable and unstable epilepsy patients, with more pronounced cost increases in patients receiving multiple generic versions. Brand-to-generic substitutions of AEDs do not necessarily reduce overall healthcare costs and may even increase them.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
GPT teacher head0.455
Teacher spread0.202 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2009
Admission routes2
Has abstractyes

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