The Correlation Between Age and Bleeding Volume in Haemorrhagic Stroke Using Multi Slice CT at District Hospitals in Jakarta
Bibliographic record
Abstract
Haemorrhagic Stroke is a common disease in Indonesia. The best imaging modality for this disease is Multi Slice Computed Tomography Scanning (MSCT), as it may help strengthening the diagnosis as well as determining the brain bleeding volume. This study aimed to show correlation between bleeding volume of the brain and patient's age using cross-sectional approach. The 68 samples in this study were taken from secondary data from Head CT Scan of Haemorrhagic Stroke cases. Brain bleeding volume is the dependent variable, obtained through slice thickness of 5 mm and ABC/2 method with software measurement in MSCT Scan device. The independent variable of this study is the patient's age. The result of the study was the average brain's bleeding volume of 21.76 ml ± 2.48 ml (range of 1.04 ml to 94.73 ml).The slice thickness using ABC/2 method, has a significant correlation with brain's bleeding volume in MSCT Scan examination, with correlation coefficient value r of 0.79. Brain bleeding volume in patients who have ages lower than 50 years and more or equal to 50 years were (18.93 ± 3.26) ml and (23.53 ± 3.47) ml respectively. There is no correlation between age and brain's bleeding volume in haemorrhagic stroke cases, with p value of 0.18, r = 0.19.
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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 | 0.001 | 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".