Retrospective cross-validation of simplified predictive index for renal replacement therapy after cardiac surgery
Bibliographic record
Abstract
OBJECTIVES: Acute kidney impairment requiring renal replacement therapy is an infrequent but dangerous complication of cardiac surgery. Its development is associated with high mortality and morbidity. A recently published simple risk stratification engine has been developed and validated in the USA and Canada, but its discriminatory power has never been tested in Europe. We aimed to cross-validate the newly developed risk stratification algorithm in a group of patients operated on in a single centre in Poland. METHODS: From electronic database we selected 1421 patients fulfilling identical inclusion and exclusion criteria as in derivation cohort in Canada. In each patient eligible for analysis we calculated simplified renal index and assessed its predictive power for the need of renal replacement therapy. RESULTS: After surgery 33 (2.3%) patients developed acute kidney impairment and subsequently underwent renal replacement therapy. The simplified renal index predicted risk of postoperative renal replacement therapy in our group. Patients with low values of simplified renal index (0-1), medium (2-3) and high values (4 and more) were found to have increasingly higher risk for renal replacement therapy of 1.1% (95% CI: 0.5-2.1%), 3.2% (95% CI: 1.9-5%) and 12.5% (95% CI: 5.2-24.1%), respectively. The area under the ROC curve of simplified renal index as predictor of renal replacement therapy in our centre was 0.73 (95% CI: 0.62-0.81) and did not differ significantly from the values obtained in the original paper. CONCLUSION: The new risk stratification algorithm is effective in discrimination of patients at high risk for development of acute kidney impairment with the need of renal replacement therapy.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".