Ethical challenges in pediatric pain research
Bibliographic record
Abstract
The history of research involving children includes both atrocities and organized attempts to protect and maintain the rights of children (e.g. Nuremberg Code, Declaration of Helsinki, Belmont Report; Matutina, 2009). Although research has moved beyond blatant violations of the rights of children, many ethical controversies remain. Children, by their nature, are vulnerable. They are not able to legally consent, there are power imbalances inherent in their relationships with adults, and their cognitive and decision-making abilities are continually developing. As pediatric pain researchers, we strive to reduce the pain and suffering of children by conducting meaningful research with children in ethically appropriate ways. Given that we investigate pain and distress among this vulnerable population, the ethics of our research methodology is often closely scrutinized. The following sections highlight some current ethical controversies facing pediatric pain researchers, namely, the exclusion of children from research, the problem of defining minimal risk, and the therapeutic misconception. While these issues span other areas of research involving children, we will illustrate their unique application and the specific challenges they pose to pediatric pain research.
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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 | 0.405 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.016 | 0.031 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".