Remote ischemic conditioning and renal function after contrast-enhanced CT scan: A randomized trial
Bibliographic record
Abstract
Purpose Remote ischemic conditioning has been shown to protect against kidney injury in animal and human studies of ischemia-reperfusion. Recent evidence suggests that conditioning may also provide protection against kidney injury caused by contrast medium. The purpose of this study was to determine if conditioning protected against increases in serum creatinine (SCr) after contrast-enhanced computed tomography (CECT). Methods A randomised controlled trial (NCT 01741896) was performed with institutional review board approval and informed patient consent. Adult in-patients undergoing abdomino-pelvic CECT were allocated to conditioned or control groups. Conditioning consisted of four cycles of five minutes of cuff-induced arm ischemia with three minutes of reperfusion applied ~40 minutes before CECT. The primary outcome was SCr change after CECT. Results Baseline characteristics were similar in both groups. For all patients, conditioning reduced the risk ratio (RR) of increased SCr; RR 0.65 (95% confidence intervals 0.41 to 1.04). The protective effect was greater and the evidence for protection stronger when analysis was restricted to patients with pre-scan reduced renal function (eGFR <90 mL/min/1.73 m2); RR 0.40 (95% confidence intervals 0.17 to 0.95). Logistic regression revealed that conditioning was the only model variable that predicted decreased SCr; odds ratio 0.24 (95% confidence intervals 0.07 to 0.84) in patients with reduced baseline eGFR. Conclusion Remote conditioning decreased the risk of CECT-associated increases in serum creatinine by 60% in patients with reduced baseline eGFR. Stratification of analysis based on baseline eGFR is warranted because benefit from conditioning will occur only when there is risk of injury.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".