Cardiac health for astronauts: current selection standards and their limitations.
Bibliographic record
Abstract
INTRODUCTION: The screening tests for coronary artery disease (CAD) for applicants and the active astronaut corps are similar to those performed in the 1960s. Due to the limited treatment and return capabilities of most space vehicles, an in-flight cardiac event would result in mission failure. Improved CAD screening of astronauts is, therefore, paramount to long-duration mission success. METHODS: Literature review was performed to compare active and retired astronaut populations to other asymptomatic low-risk cohorts. All populations were examined to determine the incidence and prevalence of CAD. Framingham risk scores were calculated in NASA's active and retired astronaut corps and compared with age- and gender-matched controls. RESULTS: The current standards used for astronaut selection have been successful in creating a cohort that has less risk than their age- and gender-matched counterparts from the general population. However, the existing astronaut cardiovascular screening and selection tests do not adequately rule out CAD for long-duration missions, and, therefore, a "significant" risk of cardiac event remains, especially as we look toward Exploration Class missions. CONCLUSIONS: The current astronaut selection and retention standards may not adequately prevent cardiac events from occurring with the longer duration flights. Future research should be directed toward increasing the primary and secondary prevention of CAD in the astronaut cohort. In the meantime, the space program should evaluate the use of more aggressive terrestrial screening tools. It is important not to remove all older, experienced pilots from spaceflight crews unless overt or predictable pathology has been clearly identified.
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 | 0.056 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".