Personal journeys to and in human genetics and dysmorphology
Bibliographic record
Abstract
Genetics has become a critical component of medicine over the past five to six decades. Alongside genetics, a relatively new discipline, dysmorphology, has also begun to play an important role in providing critically important diagnoses to individuals and families. Both have become indispensable to unraveling rare diseases. Almost every medical specialty relies on individuals experienced in these specialties to provide diagnoses for patients who present themselves to other doctors. Additionally, both specialties have become reliant on molecular geneticists to identify genes associated with human disorders. Many of the medical geneticists, dysmorphologists, and molecular geneticists traveled a circuitous route before arriving at the position they occupied. The purpose of collecting the memoirs contained in this article was to convey to the reader that many of the individuals who contributed to the advancement of genetics and dysmorphology since the late 1960s/early 1970s traveled along a journey based on many chances taken, replying to the necessities they faced along the way before finding full enjoyment in the practice of medical and human genetics or dysmorphology. Additionally, and of equal importance, all exhibited an ability to evolve with their field of expertise as human genetics became human genomics with the development of novel technologies.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".