A Dynamical Study of Risk Factors in Intracerebral Hemorrhage using Multivariate Approach
Bibliographic record
Abstract
The purpose of this study is to investigate the effects of clinical covariates to the outcome of Intracerebral Hemorrhage (ICH) patients in terms of best fitted and excellent discriminate model of binary response variable. Clinical data of 985 patients with ICH have collected using the International classification of diseases, Ninth revision codes. The diagnosis of ICH was confirmed by neuro-imaging in all patients. Univariate analysis revealed that out of 88 covariates 46 were found to be significant (p<0.05). The multivariable analysis using multiple logistic regressions, exhibited a significant negative relationship between ICH and hypertension. The improvement among ICH patients having hypertension was 0.5 (p=0.001, ARR=0.5, 95% C.I. 0.3 – 0.8). The improvement among ICH patients using antihypertensive medicine was 1.3 (p = 0.016, ARR=1.3, 95% C.I. 1.1 – 1.5). Thus present study showed that ICH has strong relationship with use of antihypertensive medicine. The improvement of patients who were using antihypertensive medicine at the time of discharge was 3.0 times (p < 0.0001, ARR=3.0, 95% C.I. 2.7 – 3.2) as compared to those who did not use antihypertensive medicine. The change in ARR from 1.3 to 3.0 times shows that the use of antihypertensive medicine and ICH outcome variable are positively associated. The change in ARR of hypertensive range of SBP also indicates that the blood pressure range and ICH outcome variable are negatively associated. The neurological symptomatology, slurred speech and double vision are important factors of proposed statistical models. Moreover, a clear decrease was found in mental status from normal to coma in applicable model. Surgery is an important part of recovery, and estimated that the improvement among the ICH patients, who were treated with surgery, was 1.4 times with significant p-value in best fitted models. The complication of pneumonia during treatment of ICH subjects has highly significant negative association with outcome variable. Present Model has 0.892 area under the curve with sensitivity (0.852), specificity (0.793) and p-value (0.204). This indicates that the model gives the impression to fit quite well for predictive performance of the ICH outcome variable and the model is excellent model.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".