Ethical Considerations Regarding Financial Incentives in Plastic Surgery-Related Health Research
Bibliographic record
Abstract
Introduction: To recruit enough patients to achieve adequate statistical power in clinical research, investigators often rely on financial incentives. The use of these incentives, however, remains controversial as they may cause patients to overlook risks associated with research participation. This concern is amplified in the context of plastic surgery where aesthetic procedures are often more desirable and are not typically covered by public or private insurance plans. Despite this, the ethical debate regarding the use of incentives has largely been absent from plastic surgery journals; therefore, efforts to summarize the existing literature in the context of plastic surgery are necessary. Methods: A narrative review of the peer-reviewed published literature was performed to identify existing articles pertaining to financial incentives in plastic surgery-related health research. Results: While incentives have the potential to improve sample sizes and promote the recruitment of under-represented patient populations, undue inducement and biased recruitment are possible. At present, there exists a paucity of empirical evidence to substantiate this. Efforts should be taken by investigators and research ethics boards (REBs) to limit the potential negative impacts of monetary compensation. Investigators should place reasonable limits on the value of incentives as well as select models associated with lower risks of undue influence and enrollment bias. When financial remuneration is offered, additional care should be taken by investigators to ensure participants are adequately informed of the risks associated with research participation. Conclusion: Current best practice recommendations suggest that proposals submitted to REBs justify the incentives used. Information regarding incentives should also be included within study consent forms and communicated as part of the informed consent process.
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 | 0.462 | 0.601 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".