ESTIMATED GLOMERULAR FILTRATION RATE AND RISK OF SURVIVAL IN ACUTE STROKE.
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of survival in acute stroke using the MDRD equation derived estimated glomerular filtration rate. DESIGN: A prospective observational cross-sectional study. SETTING: Medical wards of a tertiary care hospital. SUBJECTS: Eighty three acute stroke patients had GFR calculated within 48 hours of admission after basic data were captured. OUTCOME MEASURES: Stroke outcome was defined as either discharged or still-in-care (survived) or all cause in-hospital death. GFR was estimated by the MDRD equation, stroke severity was assessed by the Canadian Neurological Scale (CNS). Data were compared between the GFR groups of < 60 ml/min and ≥ 60 ml/min. Relative risks (RR) and odds ratios (OR) for stroke outcomes (survival and death) were estimated between the GFR groups and the homogeneity of the odds ratios among the different layers of stroke severity (CNS < 6.5 and ≥ 6.5) was determined by Breslow-Day and Tarone's test. Matanel Hazensel and Cochran's tests were used to determine conditional independence and the common odds ratio with stroke severity as a layering variable. RESULTS: No significant differences were found between the age and sex distribution of the two GFR groups. Serum urea and creatinine and CNS were significantly different between the GFR groups (p < 0.001, < 0.001, < 0.001). RR of survival and death for the GFR groups-less than 60 ml/min and above or equal to 60 ml/min were (0.425 and 1.204) and (2.360 and 0.830). The OR of survival for GFR below 60 ml/min compared to GFR above or equal to 60 ml/min was 0.353. There was homogeneity across the two layers of stroke severity (CNS score less than 6.5 and above or equal to 6.5), p = 0.612 and 0.612. CONCLUSION: Independent of stroke severity, GFR is a surrogate in the assessment of the risk of survival in acute stroke.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".