Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it