Applying EBM epistemology and the GRADE system to address practitioners' disagreements in medical malpractice allegations during COVID‐19 pandemic
Bibliographic record
Abstract
RATIONALE: The GRADE system of clinical recommendations has deontic implications and can discriminate between mandatory, prohibited, and merely permitted medical decisions. AIMS AND OBJECTIVES: The recommendation categories of the GRADE framework map onto deontological imperatives that can lead to a better understanding and management of allegations of imprudence and appropriateness of treatments. Allegations made during the worst phase of COVID-19 pandemic are used as a case study for exploring the deontic implications of GRADE. METHOD: Conceptual theoretical analysis, case study analysis, and argumentation in defence of hypotheses. RESULTS: Strong GRADE recommendations for or against treatment are justified by high-quality evidence and can be construed as ethical obligations and prohibitions. In contrast, when evidence for benefit or harm is of lower quality, GRADE yields weak, discretionary recommendations. In such grey area cases, the absence of a duty to prescribe or refuse to prescribe a requested treatment is compatible with the privilege of considering unproven but possibly beneficial options in a private setting. This privilege, however, does not extend to healthcare policymakers, who have a duty to promote actions that serve the public and whose recommendations should not be guided by personal or idiosyncratic preferences or values. CONCLUSION: If there is no prima facie evidence that a proposed treatment is harmful, doctors are not negligent in considering it in shared doctor-patient decision-making. But these clinical decisions under uncertainty do not transfer obligations to health authorities, who are not part of the decision-making process in clinical settings. The clinical decision-making process concerns particulars and is guided by contextual and specific reasons that do not fall within the scope of a general policy. Thus, in the context of a serious epidemic in which patients need and demand treatments, if the body of evidence is still changing and fragile, an attitude of tolerance and connivance may ensure a smoother transition to a more stable phase of progress, both in scientific and clinical medicine.
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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 | 0.182 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.013 | 0.069 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".