Predicting risk factors of working aged hemorrhagic stroke patients in a tertiary teaching hospital in Chiang Mai
Bibliographic record
Abstract
Stroke is the third leading cause of morbidity and mortality in Thailand accounting for a significant and increasing share of hospital costs. The purpose of this project is to study the epidemiology of the prevalence and its predicting factors of working aged hemorrhagic stroke (HS) patients admitted at a tertiary teaching hospital in Chiang Mai, Thailand. We conducted a five-year retrospective descriptive study. The subjects in this study were patients diagnosed with HS, between 15-59 years of age, and admitted to a tertiary teaching hospital in Chiang Mai, Thailand from January 2009 to December 2013. A total of 404 working aged adults who had HS were admitted to the hospital during this review period; 60.9% males and 39.1% females. Nearly 70% of patients were between 46-59 years of age (M = 47.5, SD = 9.8). Of the patients admitted to the hospital, 76.7% were transferred there from other hospitals. Intracerebral hemorrhage was present in 59.7% of patients. Severe HS occurred in 35.9% of the patients with a Glasgow Coma Score from 3-8. Approximately 69% of the working aged HS patients required surgery. The top five identified risk factors for HS were hypertension (83.4%), hyperlipidemia (38.9%), alcohol consumption (21.5%), smoking (15.3 %), and drug non-adherence (14.9%). We found significantly associated risk factors in working-aged HS by multivariate analysis among male gender (p < .001), drug non-adherence (p = .047), and hypertension (p = .048). Raising awareness to reduce risk behavior and health promotion in the community are the keynotes for health care providers in working-aged HS prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".