Bibliographic record
Abstract
Clinical research is necessary to advance medical knowledge and to test new drugs or devices. It is therefore vital to society, and ethically imperative, that clinical studies be performed if patients are to have access to safe and effective treatments. However, clinical research by its very nature involves risk. Subjects who volunteer in clinical studies may receive no benefit; in fact, they may be seriously harmed or even die as a result of their participation. Indeed, research-related injuries and deaths, though relatively few in number (1), have become the subject of much controversy in recent years (2). Thus, the protection of human research subjects must be given the highest priority by researchers, their institutions, and the government and regulatory bodies charged with overseeing the clinical research process. Central to this process is the institutional review board (IRB) in the United States (U.S.), or research ethics board as it is known in Canada. (For the purpose of this paper, the term "IRB" will be used to refer to both American and Canadian boards.) The IRB has frequently been referred to as the "first line of defense" in research subject protection (3), yet the specific roles and responsibilities of this board and its members are not clear (4). Defining these roles and responsibilities is more important now than ever before, for several reasons.
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 | 0.143 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.038 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".