Henry Friesen Award Lecture. Work, the clinician-scientist and human biochemical genetics.
Bibliographic record
Abstract
The pursuit of human biochemical genetics has allowed us to understand better how the person with the (genetic) disease differs from the disease the person has and to develop the concept that genetics belongs in all aspects of health care. It is a perspective that comes quite readily to the clinician-scientist, and the restoration of that "species" in the era of functional genomics is strongly recommended. Garrod, the initial founder of human "biochemical genetics" belonged to the clinician-scientist community. Archibald Edward Garrod introduced a paradigm, new for its day, in medicine: biochemistry is dynamic and different from the static nature of organic chemistry. It led him to think about metabolic pathways and to recognize that variation in Mendelian heredity could explain an "inborn error of metabolism." At the time, Garrod had no idea about the nature of a gene. Genes are now well understood; genomes are being described for one organism after another (including Homo sapiens) and it is understood that genomes "speak biochemistry (not phenotype)." Accordingly, in the era of genomics, biochemistry and physiology become the bases of functional genomics, and it is possible to appreciate why "nothing in biology makes sense without evolution" (and nothing in medicine will make sense without biology). Mendelian, biochemical and molecular genetics together have revealed what lies behind the 4 canonical inborn errors described by Garrod (albinisn, alkaptonuria, cystinuria and pentosuria). Both older and newer ideas in genetics, new tools for applying them (and renewed respect for the clinician-scientist) will enhance our understanding of the human biological variation that accounts for variant states of health and overt disease. A so-called monogenic phenotype (phenylketonuria) is used to illustrate, in some detail, that all disease phenotypes are, in one way or another, likely to be complex in nature. What can be known and what ought to be done, with knowledge about human genetics, to benefit individuals, families and communities (society), is both opportunity and challenge.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.201 | 0.115 |
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".