Neurons Over Nephrons
Bibliographic record
Abstract
Background and Purpose— Because of the perceived risk of contrast-induced acute kidney injury (AKI), many centers require pre-imaging serum creatinine levels, potentially delaying care. We performed a systematic review and meta-analysis evaluating AKI rates in patients with acute ischemic stroke receiving computed tomographic angiography (CTA) and computed tomographic perfusion (CTP). Methods— We searched MEDLINE, EMBASE, and the Web of Science through December 2016 for studies reporting on AKI in patients with acute ischemic stroke receiving CTA/CTP. Using a random-effects model, estimates were pooled across studies. Outcomes of interest were (1) the odds of AKI in patients receiving CTA/CTP versus noncontrast computed tomography, (2) overall rate of AKI and hemodialysis in patients with acute ischemic stroke undergoing CTA/CTP, and (3) the odds of CTA/CTP-associated AKI among patients with and without chronic kidney disease. Results— Fourteen studies were included (6 case–control studies and 8 single-arm studies) with 5727 CTA/CTP and 981 noncontrast computed tomography patients. In case–control studies, AKI was significantly lower among CTA/CTP patients compared with noncontrast computed tomography patients (odds ratio=0.47; 95% confidence interval=0.33–0.68; P <0.01). Adjusting for baseline creatinine, there was no difference in AKI rates between groups (odds ratio=0.34; 95% confidence interval=0.10–1.21). The overall rate of AKI in CTA/CTP patients was 3% (95% confidence interval=2%–4%). The overall rate of hemodialysis in the CTA/CTP group was 0.07% (3 of 4373). There was no difference in AKI among CTA/CTP patients with and without chronic kidney disease (odds ratio=0.63; 95% confidence interval=0.34–1.12). Conclusions— Nonrandomized evidence suggests that CTA/CTP are not associated with statistically significant increase in risk of AKI in patients with stroke, even those with known chronic kidney disease.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".