Cost of hospitalization for stroke in a low–middle-income country: Findings from a public tertiary hospital in the Philippines
Bibliographic record
Abstract
BACKGROUND: Determining the cost of hospitalization for acute stroke is important in the appropriate allocation of resources for public health facilities and in the cost effectiveness analysis of interventions. Despite being the second leading cause of mortality in the Philippines, there are no published data on the cost of stroke in the country. AIM: The study aims to determine the in-hospitalization cost for stroke (IHCS) in a tertiary public hospital in the Philippines and identify the factors influencing IHCS. METHODS: The study was a retrospective review of the medical and billing records of the hospital. Adult patients admitted for acute stroke between 1 June 2017 and 31 May 2018 were included in the analysis. After the mean cost of stroke was determined, multivariate logistic regression analysis was done to determine demographic and clinical characteristics that were predictive of stroke cost. RESULTS: A total of 863 patient records were analyzed. The median in-hospitalization cost for stroke was PHP 17,141.50 or US$329.52. Independent determinants of higher cost include male sex (p = 0.021), stroke type (hemorrhagic stroke, p = 0.001; subarachnoid hemorrhage, p < 0.001), lower GCS on admission (p = 0.023), surgical intervention (p < 0.001), intravenous thrombolysis (p < 0.001), infection (p < 0.001), length of hospital stay (p < 0.001), and mechanical ventilation (p = 0.008). CONCLUSION: The study provided current data on the in-hospitalization cost of acute stroke in a public tertiary hospital in the Philippines. Male sex, stroke type, lower GCS on admission, surgical intervention, intravenous thrombolysis, infection, length of hospital stay, and mechanical ventilation were independent predictors of cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".