for the Alberta Kidney Disease Network ACUTE RENAL FAILURE (ARF) ISincreasinglycommonandisas-sociatedwithhighcostsandad-verseclinicaloutcomes,includ-
Bibliographic record
Abstract
ingexcessmortality, increased lengthof hospital stay, and the requirement for chronicdialysis insurvivors.1Diverseop-tionsarecurrentlyavailable forprescrib-ingacuterenalreplacement, includingin-termittent, continuous, and extended-durationhemodialysisandhemofiltration and combinations thereof. Despite ad-vancesindialysistechnology,manyques-tions remain about how best to provide renal replacement to patientswithARF. This review will critically evaluate current evidence for the optimal dia-lytic management of ARF, present an evidence-based approach to this clini-cally important problem, and identify key areas for future research. METHODS This studywas conducted and reported inaccordancewithpublishedguidelines.2,3 Data Sources An expert librarian conducted a com-prehensive search to identify prospec-tive cohort studies of renal replace-ment therapies (RRTs) in patients with ARF. Only articles published as full manuscripts in English were consid-ered. MEDLINE (1966-October 2007), EMBASE(1988-October2007),All EBM Reviews (October 2007), and a variety of gray-literature sources (n=36) were searched (clinical trial registries, health technology assessment agencies, and manufacturer Web sites; for detailed search strategies, see theAlbertaKidney
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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".