Early-invasive strategies for the management of coronary heart disease in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: People with chronic kidney disease (CKD) are less likely to receive early-invasive management of acute coronary syndrome (ACS). The purpose of this article is to review the risks and outcomes of early-invasive versus conservative strategies, and to consider how contrast-induced acute kidney injury (CI-AKI) should factor in treatment decisions for people with CKD. RECENT FINDINGS: Numerous observational studies have characterized the prognostic importance of CI-AKI. However, recent studies illustrate that compared to the risk of AKI in individuals treated conservatively, the additional risk of kidney injury associated with invasive coronary procedures is relatively modest. Despite the risk of CI-AKI, early-invasive management of ACS has been associated with important long-term benefits. SUMMARY: These findings illustrate that the additional short-term risk of AKI associated with invasive management should be considered alongside long-term treatment effects on other clinical outcomes and should not act as a deterrent to their use. Strategies to increase the uptake of an invasive management approach, accompanied by the use of CI-AKI prevention strategies, could benefit high-risk individuals with CKD.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".