Influencia de la inadecuada alimentación en pacientes diabéticos del área de medicina interna del Hospital IESS Ambato
Bibliographic record
Abstract
De acuerdo con una investigacion realizada en el Policlinico Docente Raul Sanchez, situado en la ciudad de Pinar del Rio en el 2014 y dirigida a 98 pacientes diagnosticados con DM- 2, la estrategia de intervencion educativa resulto efectiva porque la poblacion diabetica tipo 2 del consultorio 9, perfecciono sus conocimientos con respecto a la diabetes mellitus y su educacion nutricional, lo que contribuira a que tomen medidas para prevenir las complicaciones y asumir mejor calidad de vida. Se recomienda capacitar al personal de salud en cuanto a la aplicacion de estrategias similares. (1) Segun una investigacion que se realizo en la Comunidad Canada de Bustos y el Capulin del estado de Guanajuato, Mexico, a 36 personas con diagnostico de DM tipo 2, dio resultados favorables las intervenciones educativas de prevencion y autocuidado que se puede implementar en personas con enfermedades cronico degenerativas para evitar la aparicion de complicaciones a temprano diagnostico. (2) Asimismo gracias al trabajo de investigacion que se realizo en pacientes diabeticos que acuden al Centro de Salud “Urbano”, Canton Cayambe, se logro comprender teoricamente que una alimentacion adecuada en los pacientes diabeticos es una parte fundamental en el manejo de la enfermedad, ya que con el buen control de esta se puede evitar o prevenir complicaciones. (3)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".