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Record W3122208036 · doi:10.3386/w20469

Poor Quality Drugs and Global Trade: A Pilot Study

2014· preprint· en· W3122208036 on OpenAlexafffund
Roger Bate, Ginger Zhe Jin, Aparna Mathur, Amir Attaran

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBusinessQuality (philosophy)IntermediaryConsumption (sociology)Distribution (mathematics)Pharmaceutical industryCommerceInternational tradeMarketingMedicinePharmacology

Abstract

fetched live from OpenAlex

Experts claim that some Indian drug manufacturers cut corners and make substandard drugs for markets with non-existent, under-developed or emerging regulatory oversight, notably Africa.This paper assesses the quality of 1470 antibiotic and tuberculosis drug samples that claim to be made in India and were sold in Africa, India, and five mid-income non-African countries.We find that 10.9% of those products fail a basic assessment of active pharmaceutical ingredients (API), and the majority of the failures are substandard (7%) as they contain some correct API but the amount of API is under-dosed.The distribution of these substandard products is not random: they are more likely to be found as unregistered products in Africa than in India or non-African countries.Since this finding is robust for manufacturer-drug fixed effects, one likely explanation is that Indian pharmaceutical firms and/or their export intermediaries do indeed differentiate drug quality according to the destination of consumption.

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.552
GPT teacher head0.554
Teacher spread0.002 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

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