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Record W4414660068 · doi:10.1038/s41526-025-00522-8

Evidence of effective cardiovascular countermeasures during spaceflights: insights from wearable monitoring

2025· article· en· W4414660068 on OpenAlexaff
Paniz Balali, Elena Luchitskaya, Amin Hossein, Elza Abdessater, Vitalie Faoro, Olivier Debeir, Jens Tank, Enrico G. Caiani, Philippe van de Borne, Pierre‐François Migeotte, Jérémy Rabineau

Bibliographic record

Venuenpj Microgravity · 2025
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of Waterloo
FundersRussian Academy of SciencesAgenzia Spaziale ItalianaFonds De La Recherche Scientifique - FNRSEuropean Space AgencyFonds Erasme
KeywordsSupine positionSittingHeart rateContractilityBlood pressureStroke (engine)Baseline (sea)Cardiac monitoring

Abstract

fetched live from OpenAlex

Microgravity induces profound cardiovascular changes, prompting space agencies to develop countermeasures to preserve their crewmembers' health. This study aimed to use a portable device based on electro-, impedance- and seismo-cardiography, to monitor a series of cardiovascular features in 17 cosmonauts. Our results showed that the evolution of cardiac time intervals, blood pressure, stroke volume, and cardiac systolic kinetic energy depended on the chosen baseline position. After five months in space, heart rate increased compared to the supine baseline on Earth (p = 0.013, d = 0.86) but not to the sitting position. Similarly, a marker of cardiac contractility (PEP/LVET ratio) decreased relative to the sitting baseline (p = 0.004, d = 1.09) but not the supine reference. All measured features, except heart rate, returned to baseline within three days post-landing. These findings support the efficacy of current countermeasures in facilitating rapid cardiovascular re-adaptation to terrestrial gravity.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.284
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venuenpj MicrogravitySame topicSpaceflight effects on biologyFrench-language works237,207