Comparison of MRI and CT for Detection of Acute Cerebral Haemorrahge
Bibliographic record
Abstract
Objective: To compare the diagnostic accuracy of MRI and CT for detection of acute cerebral haemorrahge. Study Design: Comparative Study Place and Duration: Mohiuddin Islamic Teaching Hospital Mirpur AJK for period of six months from 1st August, 2020 to 31stJanuary, 2021. Methodology: A total of 20 patients of both genders with age of 25 years and onward with intracerebral hemorrhage who underwent MRI following CT in the first 6 hours of onset and last known well time and admission in stroke center were included in the study. The follow up time duration for both the scans was 24 hours to 48hours. The Mc-Nemar test was used to determine the comparison between MRI and CT on contingency table. Results: The mean age of the participants was 53.26±2.45. The frequency of the male participants was 13(65%) and that of female was 7(35%). All cases were of acute ICH 20(100%). The number of positive cases on MRI and CT scan was 17, while the frequency of positive cases on MRI and negative on CT scan was 3 with the p- value of less than 0.001 and a confidence interval of 95%. (See table 1 for details). The interrater reliability for acute ICH on MRI is from 0.72 to 0.80 on k statistics. The interrater reliability for acute ICH on CT is from 0.83 to 0.92. Conclusion: It is concluded that the findings of MRI scan were more accurate than CT in the detection of acute intracerebral hemorrhage. Keywords: Acute Cerebral Hemorrhage, MRI, CT, Diagnostic Accuracy
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".