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Record W1995123216 · doi:10.20430/ete.v76i302.486

¿A quién beneficia el comercio paralelo de medicamentos en la Unión Europea?

2017· article· es· W1995123216 on OpenAlexaff
Joan Costa‐Font, Panos Kanavos

Bibliographic record

VenueEl Trimestre Económico · 2017
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsCanadian Association of Emergency Physicians
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

El comercio paralelo constituye un movimiento legal (arbitraje comercial) de productos de un país donde éstos se han puesto en circulación a otro país sin la autorización y la garantía del que posee los derechos de propiedad intelectual del producto. Este fenómeno se deriva de la aplicación del principio de subsidiariedad que lleva asociado la existencia de diferentes sistemas de regulación de precios y el de libre movilidad de bienes. En este artículo examinamos las tendencias de precios y penetración de medicamentos paralelamente importados respecto al precio del producto en el país de destino (beneficios de una mayor competencia) en 2002. En segundo lugar, establecemos una descomposición empírica de los efectos del comercio paralelo de medicamentos en los tres mercados principales de la Unión Europea en los diferentes agentes de la cadena del medicamento. Los resultados no siguieren pruebas de un efecto competitivo del comercio paralelo. Por otra parte, se observa que los principales beneficiarios son los propios importadores paralelos en la medida que el comercio paralelo genera ahorros muy limitados en la factura de los sistemas de salud, que difícilmente superan los perjuicios en la innovación farmacéutica.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.007

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 head0.047
GPT teacher head0.336
Teacher spread0.289 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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