Ethical care requires pragmatic care research to guide medical practice under uncertainty
Bibliographic record
Abstract
BACKGROUND: The current research-care separation was introduced to protect patients from explanatory studies designed to gain knowledge for future patients. Care trials are all-inclusive pragmatic trials integrated into medical practice, with no extra tests, risks, or cost, and have been designed to guide practice under uncertainty in the best medical interest of the patient. PROPOSED REVISION: Patients need a distinction between validated care, previously verified to provide better outcomes, and promising but unvalidated care, which may include unnecessary or even harmful interventions. While validated care can be practiced normally, unvalidated care should only be offered within declared pragmatic care research, designed to protect patients from harm. The validated/unvalidated care distinction is normative, necessary to the ethics of medical practice. Care trials, which mark the distinction and allow the tentative use of promising interventions necessarily involve patients, and thus the design and conduct of pragmatic care research must respect the overarching rule of care ethics "to always act in the best medical interest of the patient." Yet, unvalidated interventions offered in contexts of medical uncertainty cannot be prescribed or practiced as if they were validated care. The medical interests of current patients are best protected when unvalidated practices are restricted to a care trial protocol, with 1:1 random allocation (or "hemi-prescription") versus previously validated care, to optimize potential benefits and minimize risks for each patient. CONCLUSION: Pragmatic trials can regulate medical practice by providing (i) a transparent demarcation between unvalidated and validated care; (ii) norms of medical conduct when using tests and interventions of yet unknown benefits in practice; and eventually (iii) a verdict regarding optimal care.
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 | 0.531 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.114 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.024 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".