Cause of renal infarction
Bibliographic record
Abstract
BACKGROUND: Renal infarction can cause abrupt and severe hypertension and less frequently renal failure. Renal infarction results from disruption of renal blood flow in the main ipsilateral renal artery or in a segmental branch. Underlying mechanism is either general, 'embolic' or 'thrombophilic', or local related to primary 'renal artery lesion'. It depends on various causes. In absence of an identified cause, renal infarction is classified as 'idiopathic'. Previous studies report a significant number of 'idiopathic' renal infarction. OBJECTIVE: The aim of this study was to analyze various renal infarction causes. METHODS: Between July 2000 and June 2015, 259 consecutive patients with renal infarction were admitted to our hospital center and retrospectively identified from weekly multidisciplinary round. Main clinical and biological characteristics were extracted from clinical data warehouse. Renal imaging was reviewed by two readers unaware of the diagnosis. RESULTS: Of 259 initially identified patients, 30 were excluded owing to a lack of imaging or clinical data and 43 because iatrogenic renal infarction. In the 186 studied patients, dissection was observed in 76 patients (40.8%) and occlusion in 75 (40.3%). Renal infarction mechanisms were 'renal artery lesion' (n = 151; 81.2%), 'embolic' (n = 17; 9.1%), 'thrombophilic' (n = 11; 5.9%) and 'idiopathic' (n = 7; 3.8%). Predominant renal artery lesions were atherosclerosis disease (n = 52; 34.4%) followed by dissecting hematoma (n = 35; 23.2%) and fibromuscular dysplasia (n = 29; 19.2%). Right and left kidneys were equally involved. CONCLUSION: Renal artery lesion is the most frequent cause of renal infarction. This result underlines the need for extensive arterial exploration to identify the renal infarction mechanism and, in case of renal artery lesion, the underlying vascular disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".