Use of Decision Analysis and Economic Evaluation in Upper Extremity Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Decision analysis allows clinicians to apply evidence-based medicine to guide objective decisions in uncertain scenarios. There is no comprehensive review summarizing the various decision analysis tools used. The authors aimed to appraise and review the decision analytic models used in hand surgery. METHODS: A search of English articles on the PubMed, Ovid, and Embase databases was performed. All articles, regardless of date of publishing, were considered. Two reviewers, based on strict inclusion criteria, independently assessed each article. RESULTS: The search resulted in 5525 abstracts, which yielded 30 studies that met inclusion criteria. Included studies were grouped according to medical indications, with scaphoid fractures (n = 6) and carpal tunnel syndrome (n = 5) being the most commonly reported. Included articles used decision analysis (n = 15) and/or economic analyses (n = 23) to discuss diagnostic strategies or compare treatments. The three most common outcomes reported were utility (n = 12), cost per quality-adjusted life-year (n = 16), and quality-adjusted life-years (n = 16). The decision analysis models compared diagnostic strategies, management options, and novel treatments. CONCLUSIONS: Decision analysis is increasingly popular in hand surgery. It is useful for comparing surgical strategies through evaluation of quality-of-life outcomes and costing data. The most common model was a simple decision tree. The quality of decision analysis models can be improved with the addition of sensitivity analysis. Surgeons should be familiar with the principles of decision analysis, so that complex decisions can be evaluated using rigorous probabilistic models that combine risks and benefits of multiple strategies.
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 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 | 0.078 | 0.294 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".