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Record W4399688624 · doi:10.3389/fphys.2024.1360353

Preliminary evidence of high prevalence of cerebral microbleeds in astronauts with spaceflight experience

2024· article· en· W4399688624 on OpenAlexafffund
Ford Burles, Morgan Willson, Parker Townes, Allison Yang, Giuseppe Iaria

Bibliographic record

VenueFrontiers in Physiology · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Calgary
FundersCanadian Space Agency
KeywordsSpaceflightNeuroimagingMedicineIncidence (geometry)DiseaseCognitive impairmentRadiation exposureInternal medicineNuclear medicinePsychiatry

Abstract

fetched live from OpenAlex

Long-duration spaceflight poses a variety of health risks to astronauts, largely resulting from extended exposure to microgravity and radiation. Here, we assessed the prevalence and incidence of cerebral microbleeds in sixteen astronauts before and after a typical 6-month mission on board the International Space Station Cerebral microbleeds are microhemorrhages in the brain, which are typically interpreted as early evidence of small vessel disease and have been associated with cognitive impairment. We identified evidence of higher-than-expected microbleed prevalence in astronauts with prior spaceflight experience. However, we did not identify a statistically significant increase in microbleed burden up to 7 months after spaceflight. Altogether, these preliminary findings suggest that spaceflight exposure may increase microbleed burden, but this influence may be indirect or occur over time courses that exceed 1 year. For health monitoring purposes, it may be valuable to acquire neuroimaging data that are able to detect the occurrence of microbleeds in astronauts following their spaceflight missions.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.261
Teacher spread0.247 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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