Genetic counselling considerations with genetic/genomic testing in Neonatal and Pediatric Intensive Care Units: A scoping review
Bibliographic record
Abstract
Genetic and genomic technologies can effectively diagnose numerous genetic disorders. Patients benefit when genetic counselling accompanies genetic testing and international guidelines recommend pre- and post-test genetic counselling with genome-wide sequencing. However, there is a gap in knowledge regarding the unique genetic counselling considerations with different types of genetic testing in the Neonatal Intensive Care Unit (NICU) and the Pediatric Intensive Care Unit (PICU). This scoping review was conducted to identify the gaps in care with respect to genetic counselling for infants/pediatric patients undergoing genetic and genomic testing in NICUs and PICUs and understand areas in need of improvement in order to optimize clinical care for patients, caregivers, and healthcare providers. Five databases (MEDLINE [Ovid], Embase [Ovid], PsycINFO [Ebsco], CENTRAL [Ovid], and CINHAL [Ebsco]) and grey literature were searched. A total of 170 studies were included and used for data extraction and analysis. This scoping review includes descriptive analysis, followed by a narrative account of the extracted data. Results were divided into three groups: pre-test, post-test, and comprehensive (both pre- and post-test) genetic counselling considerations based on indication for testing. More studies were conducted in the NICU than the PICU. Comprehensive genetic counselling was discussed in only 31% of all the included studies demonstrating the need for both pre-test and post-test genetic counselling for different clinical indications in addition to the need to account for different cultural aspects based on ethnicity and geographic factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".