Benchmarking outcomes in the Neonatal Intensive Care Unit: Cytogenetic and molecular diagnostic rates in a retrospective cohort
Bibliographic record
Abstract
Genetic disease and congenital anomalies continue to be a leading cause of neonate mortality and morbidity. A genetic diagnosis in the neonatal intensive care unit (NICU) can be a challenge given the associated genetic heterogeneity and early stage of a disease. We set out to evaluate the outcomes of Medical Genetics consultation in the NICU in terms of cytogenetic and molecular diagnostic rates and impact on management. We retrospectively reviewed 132 charts from patients admitted to the NICU who received a Medical Genetics diagnostic evaluation over a 2 year period. Of the 132 patients reviewed, 26% (34/132) received a cytogenetic or molecular diagnosis based on the Medical Genetics diagnostic evaluation; only 10% (13/132) received a diagnosis during their admission. The additional 16% (21 patients) received their diagnosis following NICU discharge, but based on a genetic test initiated during hospital-stay. Mean time from NICU admission to confirmed diagnosis was 24 days. For those who received a genetic diagnosis, the information was considered beneficial for clinical management in all, and a direct change to medical management occurred for 12% (4/32). For those non-diagnosed infants seen in out-patient follow-up clinic, diagnoses were made in 8% (3/37). The diagnoses made post-discharge from the NICU comprised a greater number of Mendelian disorders and represent an opportunity to improve genetic care. The adoption of diagnostic tools, such as exome sequencing, used in parallel with traditional approaches will improve rate of diagnoses and will have a significant impact, in particular when the differential diagnosis is broad.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".