Correlation Between Lactate Dehydrogenase (LDH) Values and Aspects Score at the Beginning of Treatment in Acute Ischemic Stroke
Bibliographic record
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke comprising 87% of cases. Metabolic acidosis from hypoxia promotes anaerobic glycolysis, raising lactate dehydrogenase (LDH) levels, which reflects neuronal tissue injury and inflammation in acute ischemic stroke. The Alberta Stroke Program Early CT Score (ASPECTS) is used to assess ischemic brain injury on non-contrast CT, aiding early prognosis and treatment decisions. This cross-sectional study at Dr. Soetomo Hospital, Surabaya (February–May 2023), included 30 acute ischemic stroke patients (53.3% male, mean age 60.2 ± 7.1 years, onset 2–5 days). Exclusion criteria were prior thrombolysis, cancer, or organ failure. LDH levels were measured at admission using the Alinity C analyzer, and ASPECTS was calculated from initial CT scans. Spearman’s correlation was used for analysis. Results showed a significant inverse correlation between LDH and ASPECTS (r = -0.279, p = 0.003), indicating that higher LDH levels correspond with lower ASPECTS (larger infarcts). Mean LDH was elevated (258.75 ± 50.65 U/L, normal 120–190 U/L). Comorbidities included hypertension (90%), dyslipidemia (83.3%), and diabetes mellitus (56.7%). These findings suggest that serum LDH may be a valuable adjunct biomarker for early assessment of ischemic stroke severity when advanced imaging is unavailable, helping clinicians estimate infarct size rapidly. Further research involving larger populations is recommended to confirm LDH’s utility and to examine its combination with other biomarkers for acute ischemic stroke management.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".