Hypoalbuminemia and other prognostic factors of mortality at different time points after ischemic stroke.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate whether hypoalbuminemia and other risk factors for mortality after stroke have the same or different short (1 month), medium (3 months), long (1 year) or very long term (5 years) prognostic value. SUBJECTS/METHODS: clinical and analytical data from 254 patients admitted to our Hospital with an ischemic stroke and followed up prospectively for 2 years were collected with a prospective standard protocol. Additional data up to 5 years were obtained from Clinical and Laboratory Registries of the Hospital, a mailed questionnaire, a phone call and the Council Registry of Mortality. Risk factors for mortality at different time points were calculated with logistic regression and Cox proportional hazard analyses. RESULTS: The following factors were significantly associated with mortality at one month: cardioembolic mechanism, hypoalbuminemia, glycemia, age, low diastolic arterial pressure and Canadian Scale, at three months: previous stroke and Barthel index at discharge, at one year: previous dementia and Barthel index at three months and at five years: age, Canadian Scale score at discharge and low cholesterol at admission. Cox regression analysis considering survival time showed hypoalbuminemia at admission (hazard ratio (HR) 2; p = 0.03), age (HR 1.06; p < 0.00), previous dementia (HR 2; p < 0.00), cardioembolic mechanism (HR 2; p < 0.00) and severity on the Canadian Neurological Stroke Scale (HR 1.2; p < 0.00) to be independently associated with mortality. CONCLUSION: Mortality after ischemic stroke seems to depend on different factors along time. Hypoalbuminemia at admission is an independent factor for short term (acute) and global mortality. Other risk factors for global mortality were previous dementia, cardioembolic mechanism and severity on the Canadian Neurological Stroke Scale at admittance.
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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 | 0.003 |
| 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 | 0.000 | 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".