Clinical Practice Guideline on management of older patients with chronic kidney disease stage 3b or higher (eGFR <45 mL/min/1.73 m<sup>2</sup>)
Bibliographic record
Abstract
Acute kidney injury CKD Chronic kidney disease CKD-EPI Chronic Kidney Disease Epidemiology Collaboration CM Conservative management eGFR Estimated glomerular filtration rate ERA-EDTA European Renal Association -European Dialysis and Transplant Association ERBP European Renal Best Practice ESKD End-stage kidney disease HD Hemodialysis HR Hazard ratio KFRE Kidney Failure Risk Equation MD Mean Difference MDRD Modification of Diet in Renal Disease OR Odds Ratio PD Peritoneal dialysis QoL Quality of life RCT Randomized controlled trial REIN Renal Epidemiology and Information Network RR Relative Risk RRT Renal replacement therapy SGA Subjective global assessment 95% CI 95% Confidence Intervalcandidate.It was decided that, next to the actual members of the guideline development group, additional external experts would be approached for their expertise in specific areas.Next to setting up a guideline development group, it was decided to perform a formal scoping procedure [1] to define the topics of interest to be covered within the guideline.For this aim, a separate expert group was assembled. Expert panel scoping procedure
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".