Methodological challenges when carrying out research on CKD and AKI using routine electronic health records
Bibliographic record
Abstract
Research regarding chronic kidney disease (CKD) and acute kidney injury (AKI) using routinely collected data presents particular challenges. The availability, consistency, and quality of renal data in electronic health records has changed over time with developments in policy, practice incentives, clinical knowledge, and associated guideline changes. Epidemiologic research may be affected by patchy data resulting in an unrepresentative sample, selection bias, misclassification, and confounding by factors associated with testing for and recognition of reduced kidney function. We systematically explore the issues that may arise in study design and interpretation when using routine data sources for CKD and AKI research. First, we discuss how access to health care and management of patients with CKD may have an impact on defining the target population for epidemiologic study. We then consider how testing and recognition of CKD and AKI may lead to biases and how to potentially mitigate against these. Illustrative examples from our own research within the UK are used to clarify key points. Any research using routine renal data has to consider the local clinical context to achieve meaningful interpretation of the study findings.
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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 | 0.536 | 0.753 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".