Methodological considerations for observational studies of acute kidney injury using existing data sources
Bibliographic record
Abstract
Acute kidney injury (AKI), as defined by small, often reversible changes in kidney function, has recently been recognized as an important complication in hospitalized patients, and has been consistently associated with prolonged hospital length of stay, increased associated costs and short-term mortality. Research studies on the epidemiology of AKI must address a number of unique methodological challenges, which have the potential to impact study results and validity. This review explores several methodological issues relevant to the design and conduct of observational studies that employ preexisting laboratory, administrative or research databases and that examine AKI as an outcome or an exposure. We discuss how methodological decisions may affect study results, particularly as they relate to selection bias, misclassification and confounding. Highlighting these areas may facilitate the design of studies of high methodological rigor that advance our understanding of AKI.
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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 | 0.710 | 0.860 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".