The Validity of Using Health Administrative Data To Identify the Involvement of Specialized Pediatric Palliative Care Teams in Children with Cancer in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Population-based research to identify underserviced populations and the impact of palliative care (PC) is limited as the validity of such data to identify PC services is largely unknown. OBJECTIVE: To determine the validity of using such data to identify the involvement of specialized pediatric PC teams among children with cancer. DESIGN: Retrospective cohort. SUBJECTS: Ontario children with cancer who died between 2000 and 2012, received care through a pediatric institution with a specialized PC team and a clinical PC database. MEASUREMENTS: All patients in the clinical databases were linked to population-based health services administrative databases. Six algorithms were created to indicate the use of formal pediatric PC teams based on the record type (physician billings vs. inpatient records vs. both) and number of eligible codes required (≥1 vs. ≥2). Each was validated against the pediatric PC clinical databases. RESULTS: The cohort comprised 572 children; 243 were in the clinical databases. Algorithms using only inpatient records had high specificity (80%-95%) but poor sensitivity (21%-56%). Including physician billings increased sensitivity but lowered specificity. The algorithm with overall best performance required ≥2 physician billing or inpatient diagnosis codes indicating PC [sensitivity 0.79 (95% CI 0.73-0.84), specificity 0.58 (95% CI 0.53-0.64)]. CONCLUSIONS: Health administrative data identifies involvement of specialized pediatric PC teams with good sensitivity but low specificity. Studies using such data alone to compare patients receiving and not receiving specialized pediatric PC are at significant risk of misclassification and potential bias. Population-based PC databases should be established to conduct rigorous population-based PC research.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".