Intracranial arterial calcification is highly prevalent in hemodialysis patients but does not associate with acute ischemic stroke
Bibliographic record
Abstract
Intracranial arterial calcification (IAC) is associated with ischemic stroke in the general population but this relationship has not been examined in hemodialysis patients. We examined the factors associated with IAC and its relationship with acute ischemic stroke in this population. We retrospectively studied 490 head computed tomographic scans from 2225 hemodialysis patients presenting with neurological symptoms at our center (October 2005-May 2009). Intracranial arterial calcification was graded using a validated scoring system. Multivariate regression was used to examine the factors associated with the presence of IAC, its severity, and its ability to predict acute ischemic stroke. Weibull's survival models analyzed the relationship between IAC severity and survival. Ninety-five percent of patients with ischemic stroke had IAC vs. 83% in the nonstroke group (P=0.02). Intracranial arterial calcification severity increased with age (P<0.001), hemodialysis vintage (P<0.001), serum phosphate (P<0.05), and major comorbidities. In patients with multiple computed tomographic scans during the study period, increased IAC severity at baseline was predictive of acute ischemic stroke (P=0.05) on logistic regression analysis. High-grade and not low-grade IAC was associated with worse survival (P=0.008). Intracranial arterial calcification is highly prevalent in hemodialysis patients, especially in those with acute ischemic stroke. Its severity is prognostically significant and associated with risk factors for vascular calcification and may confer a greater risk of acute ischemic stroke. The mechanisms underlying the high incidence of ischemic stroke in this patient group require further comprehensive study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".