Exploring collaboration models between geneticists and intensivists for implementing rapid genome sequencing in critical care settings
Bibliographic record
Abstract
The availability of rapid genome sequencing (rGS) for children in a critical-care setting is increasing. This study explored the perspectives of geneticists and intensivists on optimal collaboration and division of roles when implementing rGS in neonatal and pediatric intensive care units (ICUs). We conducted an explanatory mixed methods study involving a survey embedded within an interview with 13 genetics and intensive care providers. Interviews were recorded, transcribed, and coded. Geneticists endorsed higher confidence in performing a physical exam and interpreting/communicating positive results. Intensivists endorsed highest confidence in determining whether genetic testing was appropriate, communicating negative results, and consenting. Major qualitative themes that emerged were: (1) concerns with both "genetics-led" and "intensivist-led" models with workflows and sustainability (2) shift the role of determining rGS eligibility to ICU medical professionals, (3) continued role of geneticists to assess phenotype, and (4) include genetic counselors (GCs) and neonatal nurse practitioners to enhance workflow and care. All geneticists supported shifting decisions regarding eligibility for rGS to the ICU team to minimize time cost for the genetics workforce. Exploring models of geneticist-led phenotyping, intensivist-led phenotyping for some indications, and/or inclusion of a dedicated inpatient GC may help offset the time burden of consenting and other tasks associated with rGS.
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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 | 0.068 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".