Identifying malignant and pre-malignant lesions in average-risk individuals of a predominantly African American and hispanic population in the Bronx, New York
Bibliographic record
Abstract
Diseño de un modelo multiparamétrico de riesgo de eventos microvasculares en el paciente diabético tipo 2 mediante análisis de regresión logística múltiple que permita discriminar las variables asociadas a un mayor riesgo y los individuos más susceptibles de presentar este tipo de complicaciones.Estudio observacional analítico de 60 diabéticos tipo 2. Muestreo aleatorio sistematizado aplicando un análisis de regresión logística múltiple (programa JMP del SAS Institute).Perfil poblacional: edad media: 61,15 ± 10,69 años; sexo: 36 % hombres/64 % mujeres; índice de masa corporal (IMC): 30,5±5,03; índice cintura-cadera: 0,97±0,05; hipertensión arterial: 65 %; sistólica: 143,61±16mmHg; diastólica: 83,65 ± 10,67 mmHg; dislipidemias: 70 %; eventos microvasculares: 35 %; glucemia basal: 174,63±54,53 mg/dl; HbA1c: 6,56±1,61 %; colesterol: 222,61±51,37 mg/dl; triglicéridos: 184,93 ± 15,77 mg/dl; c-HDL: 49,03 ± 1 mg/dl; microalbuminuria: 16,6 %.Diseño de un modelo de regresión logistica múltiple de eventos microvasculares, análisis de verosimilitud p < 0,0025. Están relacionadas en este modelo las siguientes variables ordenadas según su peso específico en la ecuación de riesgo: microalbuminuria, índice cintura-cadera, hipertensión arterial y glucemia basal.Se diseña un modelo multiparamétrico de riesgo de eventos microvasculares en el paciente diabético tipo 2 que considera como variables discriminativas asociadas a un mayor riesgo: indice cintura-cadera, hipertensión arterial, microalbuminuria y glucemia basal.A design of a multiparametric risk model of microvascular events in the patient diabetic type 2 with arterial hypertension by means of analysis of multiple logistical regression that allows to discriminate against the variables associated to a bigger risk and the most susceptible individuals to present this type of complications. Used methods. An observational analytic study of 60 diabetic type 2. Systematized alcatory sampling, applying a multiple logistical regression analysis (JMP of the SAS Institute program).Populational profile: half age: 61.15 ± 10.69 years; sex: 64 % women; body mass index (JMC): 30.5 ± 5.03; waist to hip index (ICC): 0.97 ± 0.05; arterial hypertension (HTA): 65 %; systolic: 143.61 ± 16 mmHg; dyastolic: 83.65 ± 10.67 mmHg; dislipaemias: 70 %; microvascular events: 35 %; blood glucose: 174.63 ± 54.53 mg/dl; HbA1c: 6.56 ± 1.61 %; cholesterol: 222.61 ± 51.37 mg/dl; triglycerids: 184.93 ± 15.77 mg/dl; c-HDL: 49.03 ± 1 mg/dl; microalbuminuria: 16.66%.A design of a multiple logistical regression model of microvascular events, likelihood ratio p < 0.002543. They are related in this model the following orderly variables according to their specific weight in the equation of risk: microalbuminuria, arterial hypertension, waist to hip index, blood glucose.A multiparametric risk model of microvascular events is designed in the patient diabetic type 2 that considers these variables associated to a bigger risk: waist to hip index, arterial hypertension, microalbuminuria and blood glucose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".