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Record W4412704756 · doi:10.1038/s41526-025-00511-x

Recovery of bone microarchitecture and density four years after spaceflight: two case studies

2025· article· en· W4412704756 on OpenAlexafffund
Bryn E. Matheson, Matthias Walle, Anna‐Maria Liphardt, Paul A. Hulme, Martina Heer, Sara R. Zwart, Jean D. Sibonga, Scott M. Smith, Leigh Gabel, Steven K. Boyd

Bibliographic record

Venuenpj Microgravity · 2025
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersCanadian Space Agency
KeywordsSpaceflightMicroarchitectureComputer scienceEngineeringAerospace engineeringParallel computing

Abstract

fetched live from OpenAlex

Spaceflight is known to negatively impact bone health, but the duration of these effects remains unclear. These two case studies investigated bone microarchitecture, density, and remodelling up to 4 years after long-duration spaceflight, aiming to inform countermeasure development and guide future research efforts. High-resolution peripheral quantitative computed tomography (HR-pQCT) and dual X-ray absorptiometry (DXA) scans were conducted on two crew members at pre-flight and up to 48-months post-spaceflight. Both crew members exhibited significant bone loss in the tibia at return from spaceflight, while only one crew member showed losses in the radius. After 4 years of recovery, one crew member achieved full recovery, while the second experienced persistent trabecular deficits that were compensated by significant cortical thickening. These results provide insight into the need for tailored countermeasures and prolonged monitoring to optimize skeletal health for future long-duration space missions, with implications for bone health research on mechanical unloading and reloading.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0030.001
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 head0.011
GPT teacher head0.307
Teacher spread0.296 · 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 designCase report
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

Citations1
Published2025
Admission routes2
Has abstractyes

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