Levels in Decision Making and Techniques for Clinicians
Bibliographic record
Abstract
In the last century, there has been tremendous advancements in medicine and surgery and this progress has resulted in an over expansion of the number of treatment options that are available to treat many conditions. The enlarging armamentarium available to modern physicians should be celebrated. However, this has come with a significant increase in the complexity of decisions that physicians have to make when they select a therapy among many that have comparable efficacy. For example, small hepatocellular carcinomas can be treated with liver transplantation, surgical resection or locoregional therapies, with similar overall survival but different disease free survival and morbidities. One of the primary goals of decision analysis is to help decision makers. In healthcare, this translates in more cost-effective treatments, higher patients’ satisfaction and overall better outcomes. Because judgments of uncertainty are a critical part of medical decision-making, decision analysis tends to improve the accuracy of these judgments by using specific algorithms and techniques. The main aim of this review is to make clinicians familiar with the different levels of decision analysis. In this paper, we will describe common techniques that are used to elicit patients’ preferences, the meaning of utilities and the benefit and limitations of decision analysis in health care.
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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 | 0.068 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".