Decision analysis in aerospace medicine: costs and benefits of a hyperbaric facility in space.
Bibliographic record
Abstract
INTRODUCTION: Assembly and maintenance of the International Space Station (ISS) requires an unprecedented number of spacewalks, which expose astronauts to the risk of decompression sickness (DCS). We illustrate the use of decision analysis to compare a hyperbaric oxygen (HBO) chamber to currently available therapy for DCS treatment on the ISS. METHODS: A decision-analytic model that simulates events over the lifespan of the ISS was constructed. Inputs to the model for probabilities, costs, and measures of morbidity and mortality were derived from a variety of sources, including a systematic literature review and an iterative consultation process with personnel at the Canadian Space Agency and the National Aeronautics and Space Administration (NASA). The decision model was analyzed using the methods of Monte-Carlo simulation and expected value calculation. Main outcome measures included the present value of costs and quality adjusted life years (QALYs), and the cumulative probability of mission-related events over the life cycle of the ISS. Sensitivity analysis was performed. RESULTS: The HBO chamber strategy is associated with a mean cost of -12.5 million dollarsw (a net cost saving of 12.5 million dollars) with a 95% CI (-112.8 million dollars, 51.3 million dollars). An HBO chamber reduces the likelihood of a premature shuttle return and a premature Soyuz return by 8% and 3%, respectively. The result is sensitive to the lifespan of the ISS. CONCLUSIONS: At a 50 million dollars cost, an HBO chamber is likely, though not certain, to result in cost savings. Decision analysis is a useful tool for use in priority setting in aerospace medicine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".