End-stage kidney disease patients in the intensive care unit
Bibliographic record
Abstract
Chronic kidney disease (CKD) is an important public health issue. In the US adult population, the prevalence of CKD is estimated at >13% (representing ∼40 million), and has increased appreciably in recent years [ 1 ]. This increasing trend has been attributable to an ageing population, along with increasing rates of diabetes mellitus, hypertension and obesity [ 1–3 ]. This mounting burden of CKD is also projected to contribute to greater numbers of patients progressing to end-stage kidney disease (ESKD) and requiring maintenance renal replacement therapy (RRT). Data provided by the United States Renal Database System (USRDS) show that both the prevalence and incidence of ESKD have increased substantially in recent years [ 4 ]. This epidemic of ESKD has far-reaching clinical and economic implications, as patients with ESKD are more likely to develop cardiovascular morbidity; serious medical complications (i.e. gastrointestinal bleeding, nutritional deficiencies, infections) [ 5–9 ]; require hospitalization; consume greater health resources; and have higher associated costs of care [ 10 ]. The attributable costs of ESKD alone in the USA in 2005 were $21 billion, representing 6.4% of all Medicare expenditures [ 11 ].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.011 |
| 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".