Marcadores tempranos en el diagnóstico de la enfermedad renal crónica en pacientes diabéticos e hipertensos: Revisión bibliográfica
Bibliographic record
Abstract
Chronic kidney disease affects 10% of the world's population, with a higher incidence in diabetics and hypertensive patients due to endothelial damage and inflammation. Diagnosis is based on a glomerular filtration rate <60 ml/min/1.73 m² or markers such as albuminuria. Early detection is key, and early biomarkers such as cystatin C, neutrophil lipocalin gelatinase-associated protein, and cell membrane glycoprotein have demonstrated greater sensitivity. This study is a descriptive and analytical analysis, using a systematic review design, and analyzing literature from scientific databases such as PubMed, SciELO, Google Scholar, and Elsevier. Recent articles in Spanish, English, and Portuguese were included, ensuring ethical principles according to the Vancouver Standards. The studies analyzed highlight that microalbuminuria, serum creatinine, and glomerular filtration rate are the main parameters used to diagnose kidney disease in diabetic patients. The study has demonstrated the effectiveness of various biomarkers for the early diagnosis of chronic kidney disease, although albuminuria remains the most widely used marker due to its easy accessibility.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.037 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".