Genetic and metabolic investigations for individuals with neurodevelopmental disorders: A survey of Canadian geneticists' practices
Bibliographic record
Abstract
Neurodevelopmental disorders (NDDs) are genetically heterogeneous. There are many possible etiological investigations for NDDs, and a lack of clear and current guidelines for such testing. Here we characterize the practices of genetic and metabolic physicians in Canada as it pertains to etiological investigation of patients with NDDs, by means of an online questionnaire. The survey response rate was 30% (n = 46). The most commonly ordered first-line tests for patients with non-syndromic NDDs are chromosomal microarray (98%) and Fragile X testing (85%). The most commonly ordered second-line test for non-syndromic NDDs is a multi-gene panel (78%) or exome sequencing (29%). Biochemical screening is ordered as a first line test by 33% of respondents, second line by 31%, and rarely or never by 36% of respondents. Those respondents with metabolics fellowship training were more likely to order biochemical screening than those without. The number of years of clinical experience generally did not affect the types of tests ordered. For patients with NDDs, test-ordering practice among Canadian clinical geneticists is highly variable, in particular with respect to biochemical screening and use of next-generation sequencing technologies. Evidence-based guidelines should be developed to facilitate best practices in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".