Trust based obligations of the state and physician-researchers to patient-subjects: Figure 1
Bibliographic record
Abstract
When may a physician enroll a patient in clinical research? An adequate answer to this question requires clarification of trust-based obligations of the state and the physician-researcher respectively to the patient-subject. The state relies on the voluntarism of patient-subjects to advance the public interest in science. Accordingly, it is obligated to protect the agent-neutral interests of patient-subjects through promulgating standards that secure these interests. Component analysis is the only comprehensive and systematic specification of regulatory standards for benefit-harm evaluation by research ethics committees (RECs). Clinical equipoise, a standard in component analysis, ensures the treatment arms of a randomised control trial are consistent with competent medical care. It thus serves to protect agent-neutral welfare interests of the patient-subject. But REC review occurs prior to enrolment, highlighting the independent responsibility of the physician-researcher to protect the agent-relative welfare interests of the patient-subject. In a novel interpretation of the duty of care, we argue for a "clinical judgment principle" which requires the physician-researcher to exercise judgment in the interests of the patient-subject taking into account evidence on treatments and the patient-subject's circumstances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.035 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".