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Record W3202495714 · doi:10.1371/journal.pmed.1003747

Evaluation of portable devices for medicine quality screening: Lessons learnt, recommendations for implementation, and future priorities

2021· article· en· W3202495714 on OpenAlexfundno aff
Céline Caillet, Serena Vickers, Vayouly Vidhamaly, Kem Boutsamay, Phonepasith Boupha, Stephen Zambrzycki, Nantasit Luangasanatip, Yoel Lubell, Facundo M. Fernández, Paul N. Newton

Bibliographic record

VenuePLoS Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Quality and Counterfeiting
Canadian institutionsnot available
FundersUniversity of OxfordDepartment of Foreign Affairs and Trade, Australian GovernmentAustralian GovernmentWellcome TrustWellcomeGovernment of the United KingdomDepartment for International DevelopmentUnited States PharmacopeiaGovernment of CanadaAsian Development Bank
KeywordsQuality (philosophy)MedicineMEDLINEMedical physicsComputer scienceData sciencePolitical science

Abstract

fetched live from OpenAlex

Portable devices able to detect substandard and falsified medicines are vital innovations for enhancing the inspection of medicines in pharmaceutical supply chains and for timely action before they reach patients. Such devices exist, but there has been little to no independent scientific evidence of their accuracy and cost-effectiveness to guide regulatory authorities in choosing appropriate devices for their settings.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.508
GPT teacher head0.571
Teacher spread0.063 · 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 designOther design
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

Citations17
Published2021
Admission routes1
Has abstractyes

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