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Evolución de la Terapia de Nutrición Enteral: Revisión de la literatura

2020· article· es· W3024942293 on OpenAlexvenueno aff
Dayana Isabel Méndez Padilla, Dunia Rueda García

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

La siguiente revisión tiene por objetivo analizar la evolución de la terapia de nutrición enteral. Teniendo en cuenta que es un procedimiento técnico de soporte nutricional en pacientes que por diversas circunstancias presentan alteraciones nutricionales y que no puedan alimentarse normalmente por vía oral. Se utilizó un método cualitativo de tipo exploratorio-documental, la bibliografía consultada para el presente trabajo fue recuperada de las publicaciones emitidas; por bases de datos bibliográficas reconocidas e indexadas de los últimos artículos en este tema. Se concluye que la nutrición enteral es un procedimiento que ha tenido avances en estos últimos 20 años siendo una técnica avanzada para pacientes con riesgo nutricional y además que se debe usar formulas específicas de alimentación de acorde a cada patología clínica del paciente con la finalidad de poder mantener una nutrición adecuada, estas fórmulas se encuentran ya en el mercado comercial como una ayuda a desarrollar esta técnica la cual no solo puede utilizarse en el ámbito hospitalaria sino también en el domicilio.

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)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.010
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.317
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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