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Record W3163973815 · doi:10.1038/s41526-021-00147-7

Bone health in spacefaring rodents and primates: systematic review and meta-analysis

2021· article· en· W3163973815 on OpenAlexafffund
Jingyan Fu, Matthew Goldsmith, Sequoia D. Crooks, Sean F. Condon, Martin Morris, Svetlana V. Komarova

Bibliographic record

Venuenpj Microgravity · 2021
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsMcGill UniversityShriners Hospitals for Children - Canada
FundersCanadian Space AgencyNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSpaceflightOsteoclastRodent modelTrabecular boneCortical boneMeta-analysisBone histomorphometryBone healthPhysiologyMedicineBiologyAnatomyPathologyInternal medicineOsteoporosisBone mineral

Abstract

fetched live from OpenAlex

Animals in space exploration studies serve both as a model for human physiology and as a means to understand the physiological effects of microgravity. To quantify the microgravity-induced changes to bone health in animals, we systematically searched Medline, Embase, Web of Science, BIOSIS, and NASA Technical reports. We selected 40 papers focusing on the bone health of 95 rats, 61 mice, and 9 rhesus monkeys from 22 space missions. The percentage difference from ground control in rodents was -24.1% [Confidence interval: -43.4, -4.9] for trabecular bone volume fraction and -5.9% [-8.0, -3.8] for the cortical area. In primates, trabecular bone volume fraction was lower by -25.2% [-35.6, -14.7] in spaceflight animals compared to GC. Bone formation indices in rodent trabecular and cortical bone were significantly lower in microgravity. In contrast, osteoclast numbers were not affected in rats and were variably affected in mice. Thus, microgravity induces bone deficits in rodents and primates likely through the suppression of bone formation.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.013
Bibliometrics0.0100.015
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.345
Teacher spread0.310 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations26
Published2021
Admission routes2
Has abstractyes

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