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Record W4386263238 · doi:10.47196/diab.v56i2sup.530

Recomendaciones en la primera consulta en personas adultas con obesidad

2022· article· es· W4386263238 on OpenAlexaboutno aff
Susana Gutt, Noelia Sforza, Alejandra Cicchitti, Jimena Coronel, Carla Gauna, Sandra González, Paula Lifszyc, Juliana Mociulsky, María Natalia Nachón, Paola Polo, Adriana Raquel Primerano, Guadalupe Vanoli, María Yuma

Bibliographic record

VenueRevista de la Sociedad Argentina de Diabetes · 2022
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

El objetivo de esta recomendación es establecer las mejores estrategias para el abordaje de la obesidad en la primera consulta de un paciente adulto con obesidad. Para ello se formularon tres preguntas PICO con respuestas basadas en el análisis de la evidencia científica disponible.
 Nuestros principales hallazgos fueron:
 • En la primera consulta de un paciente adulto con obesidad, la entrevista motivacional es más efectiva frente al abordaje tradicional para el descenso de peso dado que, además, permite reforzar la motivación del paciente y estimular su participación en un cambio de comportamiento.
 • Para el diagnóstico de obesidad, el índice de masa corporal (IMC) sigue siendo una herramienta útil y sencilla de detección, sin embargo, es imperativo ampliar la visión de la obesidad y establecer el riesgo de complicaciones en la primera consulta; para esto tanto el sistema de estadificación de Edmonton como el método ABCD son herramientas útiles adicionales al IMC.
 • La actividad física aeróbica sigue siendo recomendada por su beneficio en la pérdida de masa grasa, principalmente visceral, no obstante, al combinar una actividad física anaeróbica, los resultados son superiores a la estrategia aeróbica aislada.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.352
Teacher spread0.338 · 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.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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