Why this work is in the frame
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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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it