Carga, acceso y disparidades en enfermedad renal
Bibliographic record
Abstract
La enfermedad renal es un problema global de salud pblica; afecta a ms de 750 millones de personas en el mundo. La carga de la enfermedad renal, su deteccin y tratamiento, varan sustancialmente en el planeta. Los pases en desarrollo tienen una carga de enfermedad similar o incluso mayor que los desarrollados. En muchos escenarios, las tasas de enfermedad renal y la provision de su cuidado estn definidas por factores socioeconmicos, culturales y polticos, ocasionando disparidades significativas an en pases desarrollados, en la prevencin, pesquisa, acceso al cuidado y tratamiento de la enfermedad. El Da Mundial del Rin 2019 ofrece una oportunidad para tomar conciencia de esta enfermedad. Esta editorial, resalta estas disparidades y enfatiza el rol de las polticas pblicas y las estructuras organizacionales. Se destacan las oportunidades de entender las disparidades, para que puedan reducirse y canalizar esfuerzos para alcanzar una salud renal equitativa a nivel mundial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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; both teacher heads agree on what is shown here.
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".