Assessment of pit viper associated subclinical myocardial injury by speckle tracking imaging
Bibliographic record
Abstract
In post-snakebite patient care, cardiac function is chiefly monitored through Left Ventricular Ejection Fraction (LVEF) via traditional echocardiography. This approach fails to detect early systolic dysfunction sensitively, which is key for managing envenomation-induced myocardial damage and impacts treatment and prognosis. In a study of 37 snakebite patients, individuals were grouped by severity using the Snakebite Severity Scale (SSS). Correlations between serological markers and two-dimensional speckle tracking echocardiography (2D-STE) strain parameters were analyzed, with ROC curves assessing the diagnostic effectiveness for myocardial injury. After a venomous snakebite, the study found a significant decrease in the left ventricular endocardial global longitudinal strain (GLSendo) compared to the middle and epicardium layer (p < 0.01). The circumferential strain of the apical segment (ACS) was also lower in the affected group than in the control group (p < 0.05), with more severe envenomation associated with a great reduction in both GLSendo and the ACS (p < 0.01). The sensitivity of GLSendo for diagnosing myocardial injury due to venomous snakebite was 0.935, with the cut-off was 18.7%. The finding underscores the importance of using GLSendo as part of the standard echocardiographic assessment in patient. By providing early insight into the extent of myocardial injury and informed decisions regarding treatment and patient 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".