Bibliographic record
Abstract
Li C. Personalized medicine – the promised land: are we there yet? The delivery of personalized genomic medicine (refer Table 1 for a comparison of genomic vs genetic medicine and box 1 for glossary) hinges on obtaining personal genomic data through genome-wide association studies (GWAS) or whole-genome sequencing. After the completion of the human genome project (see box 2 for human genome projects and its derivative projects) in 2003, there appeared to be a period of euphoric optimism that as soon as the cost of sequencing the whole human genome could be brought down to an affordable range, the promise of personalized medicine would become a reality. However, inasmuch as the miraculous technological advancements are making whole-genome data acquisition an inexpensive reality, we are also starting to appreciate that making sense of the enormous amount of genomic data is a far bigger hurdle. Issues, both scientific and ethico-legal, will have to be addressed as genomic data are been pushed for clinical and direct-to-consumer utilization. Table 1. A simplified comparison of clinical genetics and the anticipated genomic medicine Clinical genetics Genomic medicine Emphasis Rare or very rare diseases Common complex diseases Genes involved Monogenic or oligogenic Often unknown Genomic changes Chromosome rearrangements, aneuploidy, copy number variants, deletions, duplications Multiple variants/polymorphisms Penetrance High Low Disease detection Diagnostic, carrier testing and pre-symptomatic testing Predictive risk assessment Pre-symptomatic testing High predictive value Low predictive value (not yet proven) Treatment Variable approaches Personalized approach based on genome info Clinical geneticist Medical doctors, well educated and trained in this specialty Minimal education and training Laboratory geneticist Usually PhDs, well educated and trained overseeing clinical service laboratories Usually PhD researchers without direct clinical involvement
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".