The economic burden of ventriculoperitoneal shunt insertion and its complications: Findings from a cohort in the Philippines
Bibliographic record
Abstract
Abstract Purpose Ventriculoperitoneal shunt (VPS) insertion is the gold standard treatment for congenital hydrocephalus, but there is little data about the cost of this procedure in developing countries. We aimed to determine the in-hospitalization cost of initial VPS insertion and its complications (malfunction and infection) and identify predictors of increased cost. Methods We performed a retrospective cohort study by reviewing the medical and financial records of pediatric patients with congenital hydrocephalus and underwent shunt surgery at our institution between 2015–2019. We also performed multivariable linear regression analysis to determine clinical characteristics that were predictive of cost. Results A total of 230 cerebrospinal fluid diversion procedures were performed on 125 patients. The mean age during index VPS insertion was 9.8 months (range: 7 days – 8 years). Over a median follow-up of 222 days, 15 patients (12%) developed shunt malfunction while 25 (20%) had a shunt infection. The mean in-hospitalization cost for all patients was PHP 94,573.50 (USD 1815). The predictors of higher cost included shunt infection (p < 0.001), shunt malfunction (p < 0.001), pneumonia (p = 0.006), sepsis (p = 0.004), and length of hospital stay (p = 0.005). Patients complicated by shunt infection had a higher mean cost (PHP 282,631.60; USD 5,425) than uncomplicated patients (PHP 40,587.20 or USD 779; p < 0.001) and patients who had shunt malfunction (PHP 87,065.70 or USD 1,671; p < 0.001). Conclusion The study provided current data on the in-hospitalization cost of VPS insertion in a public tertiary hospital in a developing country. Shunt infection, malfunction, pneumonia, sepsis, and length of hospital stay were significant 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 | 0.001 |
| 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".