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 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 | 0.061 | 0.083 |
| 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 | 0.020 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".