Low Serum Magnesium Levels Are Associated With Hemorrhagic Transformation After Mechanical Thrombectomy in Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Objective In patients with acute ischemic stroke (AIS), hemorrhagic transformation (HT) is a major complication after mechanical thrombectomy (MT). This study aimed to investigate the relationship between serum magnesium levels and HT after MT. Methods We collected 199 cases of consecutive AIS that received MT due to acute anterior circulation occlusions in our institution between January 2017 and January 2020. Baseline serum magnesium was obtained from all patients on admission before MT. The patients were divided into two groups based on the occurrence of HT. Univariate and multivariate analyses were performed to investigate whether magnesium was an independent predictor of HT. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were determined. Results Of the 199 enrolled patients, 40 (20.1%) presented with HT, and 12 (6%) developed symptomatic intracranial hemorrhage (sICH). Patients with HT had lower serum magnesium levels compared to those without HT (0.76 [0.69–0.80] vs. 0.84 [0.80–0.90], p < 0.001). The multivariate logistic analysis showed that the serum magnesium level (odds ratio, [OR]: 0.000, 95% confidence interval [CI]: 0.000–0.001, p < 0.001) was significantly associated with the occurrence of HT. The ROC curve analysis revealed that the serum magnesium level could predict HT with an AUC of.820 (95% CI: 0.750–0.891 p < 0.001). Serum magnesium ≤ 0.80 mmol/L could predict HT with a sensitivity of 79.2% and a specificity of 70.0%. Of interest, the serum magnesium level was not associated with HT when the baseline of serum magnesium was higher than the cut-off value (0.80 mmol/L) in the subgroup analysis. Conclusions Lower baseline serum magnesium levels (<0.80 mmol/L) on admission are associated with increased risk of HT in AIS patients receiving MT.
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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 | 0.001 | 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".