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Record W2023689383 · doi:10.4102/ajlm.v3i1.123

Improving access to new diagnostics through harmonised regulation: priorities for action

2014· article· en· W2023689383 on OpenAlexfundno aff
Ruth McNerney, Kimberly A. Sollis, Rosanna Ŵ. Peeling

Bibliographic record

VenueAfrican Journal of Laboratory Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsMarket accessBusinessRisk analysis (engineering)Quality (philosophy)Process (computing)Order (exchange)Operations managementComputer scienceComputer securityFinanceEconomics

Abstract

fetched live from OpenAlex

A new generation of diagnostic tests is being developed for use at the point of care that could save lives and reduce the spread of infectious diseases through early detection and treatment. It is important that patients in developing countries have access to these products at affordable prices and without delay. Regulation of medical products is intended to ensure safety and quality whilst balancing the need for timely access to beneficial new products. Current regulatory oversight of diagnostic tests in developing countries is highly variable and weak regulation allows poor-quality tests to enter the market. However, inefficient or overzealous regulation results in unnecessary delays, increases costs and acts as a barrier to innovation and market entry. Setting international standards and streamlining the regulatory process could reduce these barriers. Four priority activities have been identified where convergence of standards and protocols or joint review of data would be advantageous: (1) adoption of a common registration file for pre-market approval; (2) convergence of quality standards for manufacturing site inspections; (3) use of common evaluation protocols, as well as joint review of data, to reduce unnecessary duplication of lengthy and costly clinical performance studies; and (4) use of networks of laboratories for post-market surveillance in order to monitor ongoing quality of diagnostic devices. The adoption and implementation of such measures in developing countries could accelerate access to new diagnostic tests that are safe and affordable.

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)0.185
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.185
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1850.116
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.006
Science and technology studies0.0050.009
Scholarly communication0.0230.025
Open science0.0110.017
Research integrity0.0310.025
Insufficient payload (model declined to judge)0.0130.004

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 head0.043
GPT teacher head0.329
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations43
Published2014
Admission routes1
Has abstractyes

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