Big risks in small groups: The difference between epidemiology and counselling
Bibliographic record
Abstract
Congenital anomalies do not occur in all babies born after a teratogenic exposure. Whether a given exposure is teratogenic depends on the chemical nature and physical properties of the agent, the dose and route of exposure, when in pregnancy the exposure occurs, and genetic and other factors that affect susceptibility. Teratogenic birth defects are inherently multifactorial. Absolute risk, relative risk, and population attributable risk provide useful but different information regarding teratogenic effects. Statistical significance and clinical significance also are important considerations, but they may not be concordant. Demonstrating a teratogenic effect is easier if it is sought in a subgroup of patients in whom the effect is likely to be particularly prominent. The ability to detect a significant risk is, therefore, generally increased by subgroup analysis of epidemiology studies, but the greater the number of analyses performed, the higher the probability of finding associations that reach nominal statistical significance by chance alone. This problem is well recognized, but it is difficult to solve. The only compelling evidence for the reality of an association between maternal exposure to an agent during pregnancy and teratogenic effects in the children is replication of the findings in independent studies, but this is hard to obtain. As a consequence, there are very few exposures for which the available information is sufficient to make evidence-based recommendations regarding the clinical management of teratogenic risks. It is important to admit these limitations and to learn more about exposures that cause birth defects and how to prevent them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".