Genetic Testing among Children in a Complex Care Program
Bibliographic record
Abstract
Little is known about the pattern of genetic testing and frequency of genetic diagnoses among children enrolled in structured complex care programs (CCPs). Such information may inform the suitability of emerging genome diagnostics for this population. The objectives were to describe the proportion of children with undiagnosed genetic conditions despite genetic testing and measure the testing period, types and costs of genetic tests used. A retrospective analysis of 420 children enrolled in Toronto’s Hospital for Sick Children’s CCP from January 2010 until June 2014 was conducted. Among those who underwent genetic testing (n = 319; 76%), a random sample of 20% (n = 63) was further analyzed. A genetic diagnosis was confirmed in 48% of those who underwent testing. Those with no genetic diagnosis underwent significantly more genetic tests than those with a confirmed genetic diagnosis [median interquartile range (IQR): six tests (4–9) vs. three tests (2–4), p = 0.002], more sequence-level tests and a longer, more expensive testing period than those with a genetic diagnosis [median (IQR): length of testing period: 4.12 years (1.73–8.42) vs. 0.35 years (0.12–3.04), p < 0.001; genetic testing costs C$8496 ($4399–$12,480) vs. C$2614 ($1605–$4080), p < 0.001]. A genetic diagnosis was not established for 52% of children. Integrating genome-wide sequencing into clinical care may improve diagnostic efficiency and yield in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".