Quality of life in children with hydrocephalus: results from the Hospital for Sick Children, Toronto
Bibliographic record
Abstract
OBJECT: Children with hydrocephalus face several quality of life (QOL) issues that have been poorly studied. The authors' aim was to quantify the QOL for children with hydrocephalus and identify predictors of long-term outcome, using a reliable and validated outcome measure: the Hydrocephalus Outcome Questionnaire (HOQ). METHODS: All children (5-18 years old) with treated hydrocephalus attending the neurosurgery outpatient clinic at the Hospital for Sick Children were asked to participate. The patient's QOL was measured by the parent-completed HOQ. Predictor variables were extracted from the medical records. Multivariable linear regression was used to identify those predictor variables that were significantly associated with outcome. RESULTS: There was an 89% participation rate, with a total of 346 children participating (mean age 11.7 years, mean duration since diagnosis 9.9 years). Their mean HOQ Overall Health score was 0.68 (on a scale of 0 [worst QOL] to 1.0 [best QOL]). On multivariable analysis, the following predictors were associated with a worse overall QOL: increased seizure frequency, increased length of stay (LOS) in the hospital for the initial treatment of hydrocephalus, increased LOS for treatment of shunt infection and shunt overdrainage, increased number of proximal shunt catheters in situ, and increased distance of the family residence from the pediatric neurosurgical center. CONCLUSIONS: For the first time, these results establish baseline QOL values for a typical large group of children many years after their diagnosis of hydrocephalus, by using a validated and reproducible outcome measure. Many of the factors that adversely impact QOL appear to be related to shunt complications and might, therefore, be modifiable.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".