Understanding health economic analysis in critical care
Bibliographic record
Abstract
PURPOSE OF REVIEW: The article reviews the methods of health economic analysis (HEA) in clinical trials of critically ill patients. Emphasis is placed on the usefulness of HEA in the context of positive and 'no effect' studies, with recent examples. RECENT FINDINGS: The need to control costs and promote effective spending in caring for the critically ill has garnered considerable attention due to the high cost of critical illness. Many clinical trials focus on short-term mortality, ignoring costs and quality of life, and fail to change clinical practice or promote efficient use of resources. Incorporating HEA into clinical trials is a possible solution. Such studies have shown some interventions, although expensive, provide good value, whereas others should be withdrawn from clinical practice. Incorporating HEA into randomized controlled trials (RCTs) requires careful attention to collect all relevant costs. Decision trees, modeling assumptions and methods for collecting costs and measuring outcomes should be planned and published beforehand to minimize bias. SUMMARY: Costs and cost-effectiveness are potentially useful outcomes in RCTs of critically ill patients. Future RCTs should incorporate parallel HEA to provide both economic outcomes, which are important to the community, alongside patient-centered outcomes, which are important to individuals.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".