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Record W4405632920 · doi:10.1126/sciadv.adq3632

Tracking of spaceflight-induced bone remodeling reveals a limited time frame for recovery of resorption sites in humans

2024· article· en· W4405632920 on OpenAlexafffund
Matthias Walle, Leigh Gabel, Danielle E. Whittier, Anna‐Maria Liphardt, Paul A. Hulme, Martina Heer, Sara R. Zwart, Scott M. Smith, Jean D. Sibonga, Steven K. Boyd

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersAlberta InnovatesStrong
KeywordsSpaceflightResorptionBone remodelingBone resorptionMedicineWeightlessnessInternal medicine

Abstract

fetched live from OpenAlex

Mechanical unloading causes bone loss, but it remains unclear whether disuse-induced changes to bone microstructure are permanent or can be recovered upon reloading. We examined bone loss and recovery in 17 astronauts using time-lapsed high-resolution peripheral quantitative computed tomography and biochemical markers to determine whether disuse-induced changes are permanent. During 6 months in microgravity, resorption was threefold higher than formation. Upon return to Earth, targeted bone formation occurred in high mechanical strain areas, with 31.8% of bone formed in the first 6 months after flight at sites resorbed during spaceflight, significantly higher than the 2.7% observed 6 to 12 months after return. Limited bone recovery at resorption sites after 6 months on Earth indicates a restricted window for reactivating bone remodeling factors in humans. Incomplete skeletal recovery may arise from these arrested remodeling sites, representing potential targets for new interventions, thus providing means to counteract this long-term health risk for astronauts.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.349
Teacher spread0.314 · 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

Citations16
Published2024
Admission routes2
Has abstractyes

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