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 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 | 0.002 |
| 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) | 0.001 | 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".