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Record W4310181375 · doi:10.3390/ijerph192315866

Commentary: Zebrafish as a Model for Osteoporosis—An Approach to Accelerating Progress in Drug and Exercise-Based Treatment

2022· article· en· W4310181375 on OpenAlexaff
Natnaiel M. Dubale, Carolyn Kapron, Sarah West

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsTrent University
Fundersnot available
KeywordsOsteoporosisPsychological interventionMedicineDiseaseZebrafishIntensive care medicinePopulationDrug discoveryRisk analysis (engineering)Physical therapyBioinformaticsPhysical medicine and rehabilitationPathologyPsychiatryEnvironmental healthBiology

Abstract

fetched live from OpenAlex

Osteoporosis (OP) is a degenerative disease characterized by reduced bone strength and increased fracture risk. As the global population continues to age, the prevalence and economic burden of osteoporosis can be expected to rise substantially, but there remain various gaps in the field of OP care. For instance, there is a lack of anti-fracture drugs with proven long-term efficacy. Likewise, though exercise remains widely recommended in OP prevention and management, data regarding the safety and efficacy for patients after vertebral fracture remain limited. This lack of evidence may be due to the cost and inherent difficulties associated with exercise-based OP research. Thus, the current research landscape highlights the need for novel research strategies that accelerate OP drug discovery and allow for the low-cost study of exercise interventions. Here, we outline an example of one strategy, the use of zebrafish, which has emerged as a potential model for the discovery of anti-osteoporosis therapeutics and study of exercise interventions. The strengths, limitations, and potential applications of zebrafish in OP research will be outlined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.064
GPT teacher head0.385
Teacher spread0.321 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2022
Admission routes1
Has abstractyes

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