Physician Recruitment of Patients to Non-Therapeutic Oncology Clinical Trials: Ethics Revisited
Bibliographic record
Abstract
Tailoring medical treatment to individual patients requires a strong foundation in research to provide the data necessary to understand the relationship between the disease, the patient, and the type of treatment advocated for. Non-therapeutic oncology clinical trials studying therapeutic resistance require the participation of patients, yet only a small percentage enroll. Treating physicians are often relied on to recruit patients, but they have a number of ethical obligations that might be perceived as barriers to recruiting. Concepts such as voluntariness of consent and conflicts of interest can have an impact on whether physicians will discuss clinical trials with their patients and how patients perceive the information. However, these ethical obligations should not be prohibitive to physician recruitment of patients - precautions can be taken to ensure that patients' consent to research participation is fully voluntary and devoid of conflict, such as the use of other members of the research team than the treating physician to discuss the trial and obtain consent, and better communication between researchers, clinicians, and patients. These can ensure that research benefits are maximized for the good of patients and society.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.476 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".