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Record W2594905728 · doi:10.5539/mas.v11n4p65

Comparison of in Vitro Young and Old Lumbar Vertebrae of Ewes Related to Bone Density and Compression Strength

2017· article· en· W2594905728 on OpenAlexvenueno aff
Sahar Ahmed Abdalbary, Sherif M. Amr

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsBone mineralLumbar vertebraeLumbarCompression (physics)Bone densityAnatomyMedicineFixation (population genetics)Compressive strengthOrthopedic surgeryOrthodonticsOsteoporosisMaterials scienceSurgeryInternal medicineComposite material

Abstract

fetched live from OpenAlex

An animal model (the ewe) was used to study the mechanical properties of lumbar vertebrae and to compare these properties with the bone mineral density. We measured the bone mineral density for lumbar vertebrae of 10 ewes for L2 & L5 for5 young ewes mean age 2 years and 5 old ewes mean age 8 years old. Compression test was conducted on ewes lumbar vertebrae L2 & l5 for young ewes and old ewes.There was significance differences between both group related to bone mineral density , compressive force , and young’s modulus. There was strong correlation between the mechanical properties and bone mineral density.Bone mineral density correlated with the mechanical properties and it is not surprising that an orthopedic device used with poorly mineralized bone can have lower mechanical fixation strength than the same device with well-mineralized bone.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.336
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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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