A 5-Item Prediction Rule to Identify Severe Renal Dysfunction in Patients with Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although patients with severe renal dysfunction who receive iodinated contrast are at high risk of CIN, contrast-enhanced CT scans are often obtained without prior knowledge of kidney function in patients with acute stroke. We aimed to develop a tool to identify patients with acute stroke at a high risk of CIN in the absence of a recent GFR. MATERIALS AND METHODS: We used the RCSN (9872 patients) and OSA (2544 patients) for our derivation and validation cohort, respectively. A multivariable logistic regression model was performed to develop a predictive tool to identify severe renal dysfunction (defined as a GFR < 30 mL/min/1.73 m(2)). RESULTS: The overall prevalence of severe renal dysfunction was 4.9% and 5.2% in the derivation and validation cohort, respectively. The prediction rule was designed as follows: (age in years) + (5 points for women) + (5 points for history of diabetes mellitus) + (15 points for preadmission insulin use) + (10 points for history of hypertension). The prevalence of severe renal dysfunction is negligible in patients with a total score of ≤70 (≤0.005%-0.7%) but increases with higher Renal Risk Scores (eg, scores 71-80: 2.1%-2.2%; scores 91-100: 6.6%-7.1%; scores 111-120: 15.9%-28.1%). CONCLUSIONS: The Renal Risk Score is a validated tool that helps clinicians select which patients with stroke can safely proceed to contrast-enhanced brain imaging without waiting for laboratory evidence of good renal function.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".