Contrast-induced nephropathy: the wheel has turned 360 degrees
Bibliographic record
Abstract
Contrast-induced nephropathy (CIN) has been a hot topic during the last 5 years due its association with increased morbidity and mortality. CIN is an important complication, particularly in patients with advanced chronic kidney disease (CKD) associated with diabetes mellitus. Methods to diminish the incidence of CIN have been highly contentious. They include choice of contrast, pharmacologic manipulation, and volume expansion. The pathophysiology of this complication remains uncertain, but reduction in renal blood flow and direct toxicity of tubular cells has been implicated. More than 900 publications under the heading CIN have been published during the last 5 years. Fewer than 5% of these publications are randomized prospective controlled studies. In spite of the large number of reports on CIN, very little has been changed. The use of the smallest possible dose of low- or iso-osmolar contrast media, volume expansion, stopping nephrotoxic drugs, and avoiding repeat contrast injections within 48 hours remain the most effective approach to reduce the risk of CIN.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".