The Association Between Hyperparathyroidism and Ischemic Stroke Subtypes
Bibliographic record
Abstract
Background: Up to date, a substantial amount of research has remarked on the potential role of parathormone (PTH) in the development of subclinical and clinical vascular diseases. However, the association between the hyperparathyroidism and cerebrovascular disease has rather been underestimated in the literature. Herein, we aimed to investigate the association between serum PTH levels and ischemic stroke. Methods: Serum PTH levels were measured in all patients with ischemic stroke who were hospitalized in the Yozgat City Hospital between January 1, 2017 and January 1, 2019. Clinical and demographic findings were retrospectively evaluated via computer-based patient record system of Yozgat City Hospital (AKGUN). Results: Overall, 158 patients with ischemic stroke with a median age of 71.5 ± 11.5 were enrolled in this study. Parathyroid hormone was found to be high in 31 of the patients (19.6%). The stroke subtype of extracranial atherosclerosis was found to be more common in the group of patients with a high level of PTH (12%/3%; P = 0.008). Remarkably, logistic regression analyses also confirmed that high PTH level was a significant variable in the determination of the stroke subtype of extracranial atherosclerosis (P = 0.024). Conclusions: We have found a high rate of hyperparathyroidism in our group of patients with ischemic stroke. Remarkably, the elevation of PTH was found to be significantly associated with the ischemic stroke subtype of extracranial atherosclerosis. Clarification of these results in the future large-scale studies may provide crucial perspectives regarding our understanding of the pathophysiology of some subtypes of ischemic stroke and potentially lead to a large public health implication in this area. J Neurol Res. 2020;10(1):7-12 doi: https://doi.org/10.14740/jnr564
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".