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Fuerza muscular en la prevención de lesiones y el alta deportivo

2021· article· es· W4200314434 on OpenAlexaff
Tyron Eduardo Moreira López, Diego José Cuichan Núñez, Santos Domingo Bravo Loor

Bibliographic record

VenueRECIMUNDO · 2021
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicBusiness, Education, Mathematics Research
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

La producción de fuerza en el hombre es imprescindible para su desarrollo dentro del medio que le rodea y para la adaptación al mismo. En la realización del deporte profesional y amateur la fuerza constituye un componente a tomar en cuenta para el correcto desenvolvimiento del atleta, permite desarrollar el deporte con mejor nivel de competitividad y tiene un rol importante en la prevención de lesiones. Para este trabajo se estudia a atletas profesionales y amateurs que presentan lesiones en miembro inferior, valorando y comparando la fuerza bilateral por medio de la utilización de dinamómetro, para determinar la fuerza medida en kilogramos durante contracciones isométricas en duración de 5 segundos. Se determina que post lesión la fuerza muscular del lado afectado disminuye, produciendo una diferencia mayor al 15%, y a la vez se concluye que correcto plan de fortalecimiento permite disminuir la diferencia de fuerza y permite acondicionar al atleta para conceder el alta deportiva, lo que va a disminuir las posibilidades de una recidiva o lesiones desencadenadas por alteraciones o desequilibrios en la fuerza muscular.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueRECIMUNDOSame topicBusiness, Education, Mathematics ResearchFrench-language works237,207