Bibliographic record
Abstract
Renal biopsy (RB) is useful for diagnosis and therapy guidance of renal diseases but incurs a risk of bleeding complications of variable severity, from transitory haematuria or asymptomatic hematoma to life-threatening hemorrhage. Several risk factors for complications after RB have been identified, including high blood pressure, age, decreased renal function, obesity, anemia, low platelet count and hemostasis disorders. These should be carefully assessed and, whenever possible, corrected before the procedure. The incidence of serious complications has become low with the use of automated biopsy devices and ultrasound guidance, which is currently the "gold standard" procedure for percutaneous RB. An outpatient biopsy may be considered in a carefully selected population with no risk factor for bleeding. However, controversies persist on the duration of observation after biopsy, especially for native kidney biopsy. Transjugular RB and laparoscopic RB represent reliable alternatives to conventional percutaneous biopsy in patients at high risk of bleeding, although some factors limit their use. This aim of this review is to summarize the issues of complications after RB, assessment of hemorrhagic risk factors, optimal biopsy procedure and strategies aimed to minimize the risk of bleeding.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".