Evaluating the use of parental reports to estimate health care resource utilization in children with suspected genetic disorders
Bibliographic record
Abstract
OBJECTIVE: A key step in evaluating the cost-effectiveness of diagnostic genome-wide sequencing (GWS) services is to measure the cost of prior and subsequent diagnosis-related health care resource utilization by patients. The majority of patients using diagnostic GWS services are children, and parental surveys are often used to complement utilization data abstracted from medical records. The objective of this study was to evaluate the validity of parental reports for children with very high levels of resource utilization. METHOD: Primary caregivers of children enrolled in the CAUSES Research Clinic, a diagnostic GWS programme at B.C. Children's Hospital, completed an online survey. Parent-reported health care encounters for the 6-month period prior to survey completion were compared to utilization data abstracted from electronic medical records (EMR). The association between demographic characteristics and the probability of survey completion was tested using logistic regression. Agreement between parent-reported and EMR data was evaluated using Cohen's kappa, prevalence- and bias-adjusted kappa (PABAK), and the intraclass correlation coefficient (ICC). RESULTS: There were no major differences in demographic characteristics or resource utilization levels between families that completed the survey and those who did not. Agreement between parental reports and EMR data was high for hospitalizations (κ = 0.71; PABAK = 0.89; ICC = 0.77) but lower for outpatient physician visits (κ = 0.21; PABAK = 0.48; ICC = 0.27). CONCLUSIONS: Parental surveys are a valuable tool for estimating health care resource utilization during a 6-month recall period for children with suspected genetic disorders but are best used to complement utilization data collected from other sources.
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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 | 0.036 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".