Decision Modeling for Economic Evaluation in Otolaryngology–Head and Neck Surgery: Review of Techniques
Bibliographic record
Abstract
Decision making in health care is complex, and substantial uncertainty can be involved. Structured, systematic approaches to the integration of available evidence, assessment of uncertainty, and determination of choice are of significant benefit in an era of "value-based care." This is especially true for otolaryngology-head and neck surgery, where technological advancements are frequent and applicable to an array of subspecialties. Decision analysis aims to achieve these goals through various modeling techniques, including (1) decision trees, (2) Markov process, (3) microsimulation, and (4) discrete event simulation. While decision models have been used for decades, many clinicians and researchers continue to have difficulty deciphering them. In this review, we present an overview of various decision analysis modeling techniques, their purposes, how they can be interpreted, and commonly used syntax to promote understanding and use of these approaches. Throughout, we provide a sample research question to facilitate discussion of the advantages and disadvantages of each technique.
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 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 | 0.029 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".