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Record W3102778688 · doi:10.1186/s12910-020-00560-9

Ethics framework for treatment use of investigational drugs

2020· article· en· W3102778688 on OpenAlexaboutno aff
Jan Borysowski, Andrzej Górski

Bibliographic record

VenueBMC Medical Ethics · 2020
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophy of medicineScope (computer science)European unionExpanded accessMedicineInvestigational DrugsClinical trialBusinessPolitical scienceInternet privacyAlternative medicineComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Expanded access is the use of investigational drugs (IDs) outside of clinical trials. Generally it is performed in patients with serious and life-threatening diseases who cannot be treated satisfactorily with authorized drugs. Legal regulations of expanded access to IDs have been introduced among others in the USA, the European Union (EU), Canada and Australia. In addition, in the USA an alternative to expanded access is treatment under the Right-to-Try law. However, the treatment use of IDs is inherently associated with a number of ethically relevant problems. MAIN TEXT: The objective of this article is to present a coherent framework made up of eight requirements which have to be met for any treatment use of an ID to be ethical. These include a justified need for the use of an ID, no threat to clinical development of the ID, adequate scientific evidence to support the treatment, patient's benefit as the primary goal of the use of an ID, informed decision of a patient, fair access of patients to IDs, independent review, as well as the dissemination of treatment results. CONCLUSIONS: While this framework is essentially consistent with the legal regulations of expanded access of the USA, the EU, Canada and Australia, it is substantially wider in scope because it addresses some important issues that are not covered by the regulations. Overall, the framework that we developed minimizes the risks and threats, and maximizes potential benefits to each of the four key stakeholders involved in the treatment use of IDs including patients, doctors, drug manufacturers, and society at large.

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)0.120
metaresearch head score (Gemma)0.079
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0070.027
Scholarly communication0.0130.007
Open science0.0040.008
Research integrity0.0170.013
Insufficient payload (model declined to judge)0.0030.002

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 head0.325
GPT teacher head0.430
Teacher spread0.105 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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