The Quality of Discharge Summaries After Acute Kidney Injury
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) increases the risk of hospital readmission, chronic kidney disease, and death. Therefore, effective communication in discharge summaries is essential for safe transitions of care. Objective: The objectives of this study were to determine the quality of discharge summaries in AKI survivors and identify predictors of higher quality discharge summaries. Design: Retrospective chart review. Setting: Tertiary care academic center in Ontario, Canada. Patients: We examined the discharge summary quality of 300 randomly selected adult patients who survived a hospitalization with AKI at our tertiary care hospital, stratified by AKI severity. We included 150 patients each from 2015 to 2016 and 2018 to 2019, before and after introduction of a post-AKI clinic in 2017. Measurements: We reviewed charts for 9 elements of AKI care to create a composite score summarizing discharge summary quality. Methods: We used multivariable logistic regression to identify predictors of discharge summary quality. Results: The median discharge summary composite score was 4/9 (interquartile range, 2-6). The least frequently mentioned elements were baseline creatinine (n = 55, 18%), AKI-specific follow-up labs (n = 66, 22%), and medication recommendations (n = 80, 27%). The odds of having a higher quality discharge summary (composite score ≥4/9) was greater for every increase in baseline creatinine of 25 μmol/L (adjusted odds ratio [aOR]: 1.27; 95% confidence interval [CI]: 1.03, 1.56), intrarenal etiology (aOR: 2.32; 95% CI: 1.26, 4.27), and increased AKI severity (stage 2 aOR: 2.57; 95% CI: 1.35, 4.91 and stage 3 aOR: 3.36; 95% CI: 1.56, 7.22). There was no association between discharge summary quality and the years before and after introduction of a post-AKI clinic (aOR: 0.77; 95% CI: 0.46, 1.29). Limitations: The single-center study design limits generalizability. Conclusions: Most discharge summaries are missing key AKI elements, even in patients with severe AKI. These gaps suggest several opportunities exist to improve discharge summary communication following 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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".