Bibliographic record
Abstract
Clinical Study Agreements (CSAs) can have profound effects both on the protection of human subjects and on the independence of investigators to conduct research with scientific integrity. Sponsors, institutions, and even investigators may fail to give adequate attention to these issues in the negotiation of CSAs. Despite the key role of CSAs in structuring ethically important aspects of research, they remain largely unregulated and unreviewed for adherence to ethical norms. Academic institutions routinely enter into research contracts that fail to meet adequate ethical standards. This is a failing that can have serious consequences. Accordingly, it is necessary that some independent body have the authority both to review research contracts for compliance with norms of subject protection and ethical integrity, and to reject studies that fail to meet ethical standards. Such review should take place prior to the start of research, not later. Because of its expertise and authority, the institutional ethics review board (IRB or REB) is the appropriate body to undertake such review. Much recent commentary has focused on contractual restrictions on the investigator's freedom to publish research findings. The Olivieri experience, and that of other investigators, has brought freedom of publication issues into sharp focus. Clinical study agreements also raise a number of other ethical issues relating to human subjects and research integrity, however, including disclosures relating to patient safety, data analysis and reporting, budget, confidentiality, and premature termination of the study. This paper describes the ethical issues at stake in structuring such agreements and suggests ethical standards to guide institutional ethics review.
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 | 0.443 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.015 |
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".