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Record W2768310178 · doi:10.3791/56346

Proper Positioning and Restraint of a Rat Hind Limb for Focused High Resolution Imaging of Bone Micro-architecture Using <em>In Vivo</em> Micro-computed Tomography

2017· article· en· W2768310178 on OpenAlexafffund
Amanda B. Longo, Sandra M. Sacco, Wendy E. Ward

Bibliographic record

VenueJournal of Visualized Experiments · 2017
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of CanadaBrock University
KeywordsIn vivoPreclinical imagingBiomedical engineeringX-ray microtomographyHindlimbComputer scienceTomographyComputed tomographyMedicineAnatomyRadiologyBiology

Abstract

fetched live from OpenAlex

The use of in vivo micro-computed tomography (µCT) is a powerful tool which involves the non-destructive imaging of internal structures at high resolutions in live animal models. This allows for repeated imaging of the same rodent over time. This feature not only reduces the total number of rodents required in an experimental design and thereby reduces the inter-subject variation that can arise, but also allows researchers to assess longitudinal or life-long responses to an intervention. To acquire high quality images that can be processed and analyzed to more accurately quantify outcomes of bone micro-architecture, users of in vivo µCT scanners must properly anesthetize the rat, and position and restrain the hind limb. To do this, it is imperative that the rat be anesthetized to a level of complete relaxation, and that pedal reflexes are lost. These guidelines may be modified for each individual rat, as the rate of isoflurane metabolism can vary depending on strain and body size. Proper technique for in vivo µCT image acquisition enables accurate and consistent measurement of bone micro-architecture within and across studies.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.032
GPT teacher head0.395
Teacher spread0.363 · 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 designBench or experimental
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

Citations4
Published2017
Admission routes2
Has abstractyes

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