Perfil de pacientes com obesidade grau III atendidos em um centro de referência em obesidade
Bibliographic record
Abstract
DOI: http://dx.doi.org/10.31011/1519-339X.2018a18n84.1 Objetiva-se caracterizar o perfi l de pacientes com obesidade grau III atendidos em um Centro de Referênciaem Obesidade. Estudo retrospectivo e descritivo, realizado por meio de análise documental de 310 prontuáriosde pacientes atendidos no Centro de Referência em Obesidade do Rio de Janeiro, no período de maio de 2012a fevereiro de 2015, seguido de análise descritiva dos dados utilizando o programa Microsoft Offi ce Excel. Osresultados mostraram a predominância do sexo feminino com 234 (75%) e 76 (25%) do sexo masculino. A idadevariou entre 18 e 73 anos, com média e desvio padrão de 43,98 ± 12,41 anos. Quanto ao IMC, obteve-se a médiade 51,70 ± 7,73 kg/m2. Constatou-se alta prevalência de hipertensão arterial, com 242 (78%) pacientes e DiabetesMellitus com 149 (48%), ainda 136 (43%) possuem hipertensão e diabetes associadas. Conclui-se que as estratégiasde assistência individualizada, subsidiadas pelo conhecimento das características relacionadas aos aspectos sociaise às comorbidades associadas à obesidade, são fundamentais para prevenção de complicações e melhoria daqualidade de vida em pacientes obesos.Palavras-chave: Obesidade Mórbida; Enfermagem; Hipertensão; Diabetes Mellitus. ABstRACtAimed to characterize the profi le of patients with obesity grade III attended at a Reference Center in Obesity. Aretrospective and descriptive study carried out through documentary analysis of 310 medical records of patientsattended at the Reference Center in Obesity of Rio de Janeiro, from may 2012 to February 2015, followed by adescriptive analysis of the data using the Microsoft Offi ce Excel. The results showed a female predominancewith 234 (75%) and 76 (25%) male. The age varied between 18 and 73 years, average and standard deviationof ± 12.41 43.98 years. About the BMI, average of 51.70 ± 7.73 kg/m2. It has a high prevalence of hypertension,with 242 (78%) patients and Diabetes Mellitus with 149 (48%), 136 (43%) still have hypertension and diabetes. Itconcludes that the individualized assistance strategies, funded by the knowledge of the characteristics related tosocial aspects and the comorbidities associated with obesity, are fundamental for the prevention of complicationsand improvement of the quality of life in obese patients.Keywords: Morbid Obesity; Nursing; Hypertension; Diabetes Mellitus.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".