Abstracts for the Xth World Congress of Psychiatric Genetics
Bibliographic record
Abstract
We cannot escape the history of our field and are constantly guided today by the accumulation of facts with either positive or negative valences from our past.But when did the clock start-with the domestication of animals, with Galton's musings and amoral passion for data collection about individual differences in behavior, or with the initially objective scientizing of Mendelism applied to schizophrenia but ending with a Nazi-tainted albatross around the neck of psychiatric genetics.In regard to the long quest for the distal and genetic (partial) causes of mental diseases, the conclusion that both genetic and environmental factors, none yet known in detail, provide the distal causes of mental disorders-that statement is too general to be of use to making further progress.What is needed is a confrontational approach based on evidence collected from competing 'schools of thought', and then reconciliation before some kind of omniscient and impartial Science Court.With each new tool that was developed, from pedigree-ing, to correlation, to path analysis, to segregation analysis, to electrophoresis, to liability-threshold modeling, to linkage and association, to SNPs, and to gene expression via microchip arrays, there has been a strong tendency to put our eggs into one near-sighted basket.Erik Stromgren, one of the Danish elders, cautioned us to avoid the ''tyranny'' of technology.Research into the etiologies of major mental diseases was facilitated by adopting the approach used for complex adaptive systems as pursued by those who study coronary artery disease and diabetes.But why did it take so long embrace the strategies of complex diseases, including epigenetic perspectives, and to abandon the fixation on single major locus hypotheses?Still, weights to indicate the relative importance of putative risk factors require an awareness of odds ratios and effect sizes.The challenge to our field is to join into cross-disciplinary collaborations as well as to adapt to blind alleys more rapidly with novel or borrowed strategies, thus informing therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.337 | 0.182 |
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".