Concluding Comments: Maximizing Good Patient Care and Minimizing Potential Liability When Considering Complementary and Alternative Medicine
Bibliographic record
Abstract
Our goal for this supplemental issue of Pediatrics was to consider what practitioners, parents, patients, institutions, and policy-makers need to take into account to make good decisions about using complementary and alternative medicine (CAM) to treat children and to develop guidelines for appropriate use. We began by explaining underlying concepts and principles in ethical, legal, and clinical reasoning and then used case scenarios to explore how they apply and identify gaps that remain in practice and policy. In this concluding article, we review our major findings, summarize our recommendations, and suggest further research. We focus on several key areas: practitioner and patient/parent relationships; decision-making; dispute resolution; standards of practice; hospital/health facility policies; patient safety; education; and research. Ethical principles, standards, and rules applicable when making decisions about conventional care for children apply to decision-making about CAM as well. The same is true of legal reasoning. Although CAM use has seldom led to litigation, general legal principles relied on in cases involving conventional medical care provide the starting point for analysis. Similarly, with respect to clinical decision-making, clinicians are guided by clinical judgment and the best interests of their patient. Whether a therapy is CAM or conventional, clinicians must weigh the relative risks and benefits of therapeutic options and take into account their patient's values, beliefs, and preferences. Consequently, many of our observations apply to conventional and CAM care and to both adult and pediatric patients.
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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 | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.029 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".