Carga, acceso y disparidades en enfermedad renal
Bibliographic record
Abstract
RESUMENLa enfermedad renal es un problema global de salud pública; afecta a más de 750 millones de personas en el mundo.La carga de la enfermedad renal, su detección y tratamiento, varían sustancialmente en el planeta.Los países 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 están definidas por factores socioeconómicos, culturales y políticos, ocasionando disparidades significativas aún en países desarrollados, en la prevención, pesquisa, acceso al cuidado y tratamiento de la enfermedad.El Día Mundial del Riñón 2019 ofrece una oportunidad para tomar conciencia de esta enfermedad.Esta editorial, resalta estas disparidades y enfatiza el rol de las políticas públicas 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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".