Establishing a Continuum of Acute Kidney Injury – Tracing AKI Using Data Source Linkage and Long-Term Follow-Up: Workgroup Statements from the 15th ADQI Consensus Conference
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is independently associated with the development of chronic kidney disease, endstage kidney disease and increased all-cause and cardiovascular-specific mortality. The severity of the renal insult and the development of multiple AKI episodes increase the risk of occurrence of these outcomes. Despite these long-term effects, only a minority of patients receive nephrologist follow up after an episode of AKI; those that do may have improved outcomes. Furthermore, relatively simple quality improvement strategies have the potential to change this status quo. METHODS: On this background, a working group of the 15(th) Acute Dialysis Quality Initiative (ADQI) conference applied the consensus-building process informed by review of English language articles identified through PubMed search to address questions related to the opportunities, methodological requirements and barriers for longitudinal follow-up of patients with AKI in the era of electronic health records and Big Data. RESULTS: Four consensus statements answering the key questions identified by the working group are developed. CONCLUSIONS: We have identified minimal data elements and potential data sources necessary to trace the natural history of patients from onset of AKI to long-term outcome. Minimum infrastructure and key barriers to achieving these goals are outlined together with proposed solutions.
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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 | 0.496 | 0.431 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.015 | 0.022 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".