Ethical evaluation in acute stroke decision‐making
Bibliographic record
Abstract
RATIONALE: The evidentiary standards and epistemic models of clinical care, especially those of evidence-based medicine, are dissimilar to those used in philosophy and examination of how the two systems intersect may help clinicians make more informed treatment decisions. AIMS AND OBJECTIVES: This paper examines the use of ethical frameworks in routine clinical decision-making, using the example of acute stroke treatment decisions to demonstrate that ethical evaluation is integral to clinical practice. METHOD: Utilising acute stroke care as a lens through which to examine the phenomenon of ethical evaluation in medical practice, we offer a philosophical analysis of the presence of ethical evaluation in medicine. RESULTS AND CONCLUSION: We find that the medical establishment should embrace ethical evaluation as intrinsic to medical practice and that medical training and treatment guidelines should reflect this reality. Patients deserve clarity and transparency about how physicians make determinations about their treatment, and physicians should be prepared to offer explanations for those decisions.
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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 | 0.206 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.109 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".