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Record W23660650

Legal and ethical issues associated with patient recruitment in clinical trials: the case of competitive enrolment.

2005· article· en· W23660650 on OpenAlexaff
Timothy Caulfield

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClinical trialGovernment (linguistics)BusinessThe InternetPublic relationsMarketingMedicinePolitical sciencePathology
DOInot available

Abstract

fetched live from OpenAlex

Introduction The demand for patients for clinical trials continues to increase. There are more clinical trials being done (often involving patients with similar conditions), government regulators require an increasing amount of data for the approval process, and some within industry have speculated that patients are becoming less willing to participate. These pressures have led to the development of a variety strategies to make the recruitment of patients more efficient and effective, such as the creation of research networks, the implementation of software to determine patient eligibility (1) and the use of email and the internet to find new patients. (2) Indeed, patient recruitment has become an industry. Competitive enrolment has emerged as one of the most common patient recruitment practices. Despite being ubiquitous, there is surprisingly little literature on the nature and ethical implications of competitive enrolment. This paper briefly considers the issues associated with this recruitment scheme. I will argue that competitive enrolment creates significant ethical challenges that need to be addressed by both REBs and at the level of national research ethics policy. Competitive Enrolment From the perspective of industry, patient recruitment is seen as a critical issue. In a paper written by an industry consultant, it is claimed that only 15% of clinical trials are completed on time, with over 50% of delays attributed to patient recruitment and 30% of investigator sites failing to recruit a single patient. (3) The authors also suggest that the estimated cost of patient recruitment is $1.89 billion. These costs are subject to further increases with each day's delay in bringing the product to market. (4) In another industry document it is stated that drug companies stand to lose between $600,000 and $8 million each day clinical trials delay a drug's development and launch. (5) It shouldn't be forgotten how much the industry has invested in the research and development process. Though estimates vary considerably, one paper suggests that it takes nearly eight years to develop a drug, almost twice as long as it took 20 years ago and, quoting from a study by the Tufts Center for the Study of Drug Development, $1 billion per drug, from concept to market. (6) While such figures often come from industry sources, there is no doubt that increasing access to patients and promoting patient participation in clinical trials has become an industry priority. A lot of money is at stake. For sponsoring companies, encouraging patients to participate and to complete clinical trials has a direct relationship to profit and the success of a new product. As such, it is understandable that sponsoring companies would want to devise strategies to optimize recruitment. One such strategy is competitive enrolment. Indeed, it is frequently viewed as an essential part of the overall patient recruitment plan. As noted by one recruitment consultant: We strongly recommend the use of competitive enrolment together with the inclusion of backup sites so they can be brought on board should individual sites drop below their agreed target levels. (7) Competitive enrolment is often part of the clinical trial agreement between the investigators and the sponsor of the trial--usually a pharmaceutical company. The goal, of course, is the advancement of rapid patient recruitment. It works by pitting investigating sites against one another. In return for involvement in the protocol and remuneration (which is often generous), (8) investigators agree to recruit a specific number of patients (often within a specified period of time). Such arrangements create a significant incentive for investigators to recruit patients as fast as they can. In a sense, they are in a race with other sites. If the clinical site does not meet a specified recruitment target, the sponsoring company may have the option to drop them from the protocol. …

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.192
GPT teacher head0.427
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2005
Admission routes1
Has abstractyes

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