Bibliographic record
Abstract
This paper introduces a concept and approach on bridging Prognostics and Health Management (PHM), an engineering discipline, to Space Medicine (SM) in order to mitigate the Human Health and Performance (HH&P) risks of exploration-class space missions by focusing on efforts to reduce countermeasure mass and volume and drive the risks down to an acceptable level. The paper also discusses main risks of missions such as autonomous medical care risk (i.e., mission and long-term health risk due to the inability to provide adequate medical care throughout the mission) and Behavioral Health and Performance (BH&P) risk (i.e., mission and long-term behavioral health risk). The main objective of the HH&P technologies being developed for exploration-class missions is to maintain the health of the crew and support optimal and sustained performance throughout the duration of a mission. A PHM-based technology solution augmented with predictive diagnostics capability could be the one that meets the main objective. In discussing the similarities of and differences between the PHM and SM domains, the paper explores available solutions on crew health maintenance in terms of predictive diagnostics providing early and actionable real-time warnings of impending health problems that otherwise would have gone undetected. The paper discusses the use of PHM principles and techniques with data mining capabilities to assess the value of Electronic Health Records (EHR) augmented with real-time monitoring of data for accurate predictive diagnostics on manned space exploration programs. The proposed technology concept with predictive diagnostics capability and a pilot implementation of the technology on the International Space Station (ISS) includes evaluation and augmented research/testing of the technology, which will regularly and efficiently provide advancements during the development phases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".