Development and validation of the Pediatric Stroke Quality of Life Measure
Bibliographic record
Abstract
AIM: To develop and validate a disease-specific parent proxy and child quality of life (QoL) measure for patients aged 2 to 18 years surviving cerebral sinovenous thrombosis (CSVT) and arterial ischaemic stroke (AIS). METHOD: Utilizing qualitative and quantitative methods, we developed a 75-item Pediatric Stroke Quality of Life Measure (PSQLM) questionnaire. We mailed the PSQLM and a standardized generic QoL measure, Pediatric Quality of Life Inventory (PedsQL), to 353 families. Stroke type, age at stroke, and neurological outcome on the Pediatric Stroke Outcome Measure were documented. We calculated the internal consistency, validity, and reliability of the PSQLM. RESULTS: The response rate was 29%, yielding a sample of 101 patients (mean age 9y 9mo [SD 4.30]; 69 AIS [68.3%], 32 CSVT [31.7%]). The internal consistency of the PSQLM was high (Cronbach's α=0.94-0.97). Construct validity for the PSQLM was moderately strong (r=0.3-0.4; p<0.003) and, as expected, correlation with the PedsQL was moderate, suggesting the PSQLM operationalizes QoL distinct from the PedsQL. Test-retest reliability at 2 weeks was very good (intraclass correlation coefficient [ICC] 0.85-0.95; 95% confidence interval 0.83-0.97) and good agreement was established between parent and child report (ICC 0.63-0.76). INTERPRETATION: The PSQLM demonstrates sound psychometric properties. Further research will seek to increase its clinical utility by reducing length and establishing responsiveness for descriptive and longitudinal evaluative assessment. WHAT THIS PAPER ADDS: A pediatric stroke-specific quality of life (QoL) measurement tool for assessments based on perceptions of importance and satisfaction. Moderate-to-high reliability and validity established for a new clinical scale evaluating QoL among children with stroke. Perceived QoL measured using the Pediatric Stroke Quality of Life Measure appears lower in children with neurological impairment.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".