Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will identify ethical issues arising from conflicts of interest in sponsored clinical trials, and the need for compliance with recent privacy legislation. It will guide investigators facing ethical dilemmas that compromise the integrity of their research because of conflicts of interest or a flawed consent process. Authors will learn about changes in journal editorial policies that will require registration of clinical trials and consent for publication of case reports. RECENT FINDINGS: Recently, ethics review committees and clinical investigators have violated research ethical guidelines and authors have ignored journal policies on disclosure of data in multicentre clinical trials. Published reports show selective reporting of data from clinical trials that biases the body of evidence available for clinical decision-making. Privacy laws legislate that patient consent for the use of their health information, other than for their clinical care, must be obtained explicitly. SUMMARY: Clinical trial registration and the need for consent for publication of case reports aim to restore and improve the integrity of biomedical publication. Journal policies that incorporate these changes may be persuasive in interpreting privacy laws, in a practical way, to protect patients from harm. It is difficult to eliminate all ethical problems with sponsored trials and government regulatory drug-approval processes may require 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 | 0.170 | 0.323 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".