Evidence‐Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions
Bibliographic record
Abstract
OBJECTIVE: To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. DATA SOURCES: Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. REVIEW METHODS: Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. CONCLUSIONS: Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. IMPLICATIONS FOR PRACTICE: Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".