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Record W2120622551 · doi:10.1109/tbme.2005.855703

Dual Cryogenic Fixation for Mechanical Testing of Soft Musculoskeletal Tissues

2005· article· en· W2120622551 on OpenAlexaff
Nanthan Ramachandran, Yoichi Koike, P. Poitras, David Bäckman, Hans K. Uhthoff, Guy Trudel

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2005
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsFixation (population genetics)Biomedical engineeringTendonMaterials scienceSoft tissueMaterials testingStructural engineeringOrthodonticsEngineeringSurgeryComposite materialMedicine

Abstract

fetched live from OpenAlex

Mechanical testing of soft musculoskeletal structures like tendons and ligaments are essential to medical advances. A long-standing limitation for testing these structures in isolation has been the ability to solidly fix both ends of the tendon. Cryogenic fixation technology was leveraged into the development of a dual cryogenic fixation (DCF) device. Results of the study show that the DCF allows tendons to be tested in isolation, at physiologic temperatures, with excellent reproducibility.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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 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

Citations20
Published2005
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Biomedical EngineeringSame topicSports injuries and preventionFrench-language works237,207