Implementation of an electronic genomic and family health history tool in primary prenatal care
Bibliographic record
Abstract
"The Pregnancy and Health Profile," (PHP) is a free genetic risk assessment software tool for primary prenatal providers that collects patient-entered family (FHH), personal, and obstetrical health history, performs risk assessment, and presents the provider with clinical decision support during the prenatal encounter. The tool is freely available for download at www.hughesriskapps.net. We evaluated the implementation of PHP in four geographically diverse clinical sites. Retrospective chart reviews were conducted for patients seen prior to the study period and for patients who used the PHP to collect data on documentation of FHH, discussion of cystic fibrosis (CF) and hemoglobinopathy (HB) carrier screening, and CF and HB interventions (tests, referrals). Five hundred pre-implementation phase and 618 implementation phase charts were reviewed. Documentation of a 3-generation FHH or pedigree improved at three sites; patient race/ethnicity at three sites, father of the baby (FOB) race/ethnicity at all sites, and ancestry for the patient and FOB at three sites (P < 0.001-0001). CF counseling improved for implementation phase patients at one site (8% vs. 48%, P < 0.0001) and CF screening/referrals at two (2% vs. 14%, P < 0.0001; 6% vs. 14%; P = 0.05). Counseling and intervention rates did not increase for HB. This preliminary study suggests that the PHP can improve documentation of FHH, race, and ancestry, as well as the compliance with current CF counseling and intervention guidelines in some prenatal clinics. Future evaluation of the PHP should include testing in a larger number of clinical environments, assessment of additional performance measures, and evaluation of the system's overall clinical utility.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".