Framework for bioethical assessment of an article on therapy
Bibliographic record
Abstract
OBJECT: Frameworks for scientific assessment of articles on therapy published in the medical literature have become available and will likely enhance the quality of medical research that is published in peer-reviewed journals. Comprehensive frameworks do not exist for the assessment of bioethical issues pertaining to research on human volunteers. METHODS: The authors have developed a framework consisting of ethical dimensions or questions that they suggest should be applied to assess the bioethical integrity of articles on therapy. Thirteen questions were developed and discussed in the context of current bioethical principles, and examples were applied where possible. CONCLUSIONS: The simple framework the authors have developed offers a method to assess key bioethical issues surrounding an article on therapy and probably defines the minimum standard to which such articles should be held. Many ethical questions cannot yet be answered based on available information or bioethical theories. The authors are not suggesting that their framework is comprehensive; refinements and individualization of it to fit specific studies are probably required by each clinician-researcher who designs a therapy trial and reports its results.
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 | 0.358 | 0.368 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.032 | 0.011 |
| Science and technology studies | 0.015 | 0.060 |
| Scholarly communication | 0.033 | 0.022 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".