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Record W2214906591 · doi:10.12834/vetit.271.963.2

Effect of electric current stimulation in combination with external fixator on bone healing in a sheep fracture model.

2014· article· en· W2214906591 on OpenAlexaboutno aff
Aurelio Muttini, Michele Abate, Nicola Bernabò, Francesco Cavani, Rossella Mingozzi, Umberto Tosi, Matteo Cadossi, Stefania Setti, Sandro Giannini, R Leone, Luca Valbonetti

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsnot available
Fundersnot available
KeywordsBone healingExternal fixatorBone formationMedicineOsteotomyBiomedical engineeringDentistrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Biophysical stimulations with electric and electromagnetic fields have been demonstrated to accelerate the bone-healing rate. This study has been designed to investigate the effects of electricity directly connected with the central pins of an external fixator in an experimental osteotomy model in sheep. Thirty mg/kg of tetracycline chloride were administered on the 30th and on the 45th day after surgery for histomorphometric studies. Plain radiographs were obtained in standard projections every 15 days after surgery and were analyzed with a software program (Corel Photo-Paint Pro X2, Corel Corporation, Ottawa, Canada). The specimens obtained after 60 days were examined with histological analysis. The results show that biophysical treatment with alternating electricity in combination with external fixator enhances new-bone formation. The translational value of this study, due to the similarities between ovine and human species, suggests that this treatment could be useful in speeding the bone-healing rate both in animals and humans.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicElectromagnetic Fields and Biological Effects→French-language works237,207→