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Record W7133456067

Study of the relationship between the intercondylar pressure and induced wear in domestic porcine knees.

2017· article· es· W7133456067 on OpenAlexfundno aff
Eliana Murillo Ardila, Andrea Juliana Torres Florez

Bibliographic record

VenueUniversidad Industrial de Santander · 2017
Typearticle
Languagees
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersUniversitat Autònoma de BarcelonaShriners Hospitals for ChildrenMcGill University
KeywordsStatistical analysisMaterials testingSignificant differenceHigh pressure
DOInot available

Abstract

fetched live from OpenAlex

Este proyecto de investigación, se enfoca en encontrar la relación entre los diferentes grados de osteoartritis según la clasificación ICRS y la presión detectada entre los cóndilos femorales y el platillo tibial. Para el desarrollo de este estudio se contaron con 7 muestras de rodillas cadavéricas procedentes del cerdo doméstico (Sus Scrofa Domestica), sin discriminar su sexo, edad y rodillas derechas de izquierdas. Mediante resina # 456 y su correspondiente catalizador, se elaboró el acople al banco de pruebas de la máquina de ensayos universal MTS, previamente desarrollado por ex alumnos de la Universidad Industrial de Santander. Una fuerza compresiva de 1200 N en extensión completa fue aplicada a las muestras, para posteriormente realizar pruebas a rodillas sanas y lesionadas de acuerdo a la clasificación ICRS de lesiones condrales, con elementos como el foredom y el bisturí quirúrgico. Mediante sensores de presión de la compañía Tekscan (Medical 4000), se obtuvieron datos correspondientes a presión, fuerza, área de contacto, fuerza pico y localización de esta misma, tanto en cóndilos mediales como en cóndilos laterales. Los datos obtenidos fueron analizados mediante el análisis de varianza de un solo factor, conocido como ANOVA y se determinó entre cuales grados de lesión existen variaciones significativas para las variables previamente medidas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.333
Teacher spread0.261 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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