Collaborations in medical genetics: 10‐Year history of an ongoing Vietnamese‐North American Collaboration
Bibliographic record
Abstract
In 2006, one of my Group Health Cooperative (GHC) (now Kaiser Permanente of Washington) colleagues approached me with an opportunity to go on a medical exchange to Vietnam.Our institution had a more than 30year-long collaboration, educational program, and physician exchange program focused on primary care medicine with Hue College of Medicine and Pharmacy in Vietnam.During his recent trip, my colleague met a medical geneticist, Dr. Nguyen Viet Nhan, who was working in Hue and seeking collaboration with a United States (US) based medical geneticist.This led to my first trip to Vietnam in 2007, which has been followed by nine subsequent trips and has been the basis of an ongoing and evolving collaboration between medical geneticists in Vietnam and medical geneticists, genetic counselors, and basic scientists in the United States and Canada.Our shared commitment is to provide educational support, genetic counseling, and clinical and molecular diagnostic expertise.This work has very importantly supported the development of genetic resources for patients in Vietnam and the physicians who provide this care, but it has also grown to involve the genetics communities in other Asia-Pacific countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".