Conjoined twins: A worldwide collaborative epidemiological study of the International Clearinghouse for Birth Defects Surveillance and Research
Bibliographic record
Abstract
Conjoined twins (CT) are a very rare developmental accident of uncertain etiology. Prevalence has been previously estimated to be 1 in 50,000 to 1 in 100,000 births. The process by which monozygotic twins do not fully separate but form CT is not well understood. The purpose of the present study was to analyze diverse epidemiological aspects of CT, including the different variables listed in the Introduction Section of this issue of the Journal. The study was made possible using the International Clearinghouse for Birth Defects Surveillance and Research (ICBDSR) structure. This multicenter worldwide research includes the largest sample of CT ever studied. A total of 383 carefully reviewed sets of CT obtained from 26,138,837 births reported by 21 Clearinghouse Surveillance Programs (SP) were included in the analysis. Total prevalence was 1.47 per 100,000 births (95% CI: 1.32-1.62). Salient findings including an evident variation in prevalence among SPs: a marked variation in the type of pregnancy outcome, a similarity in the proportion of CT types among programs: a significant female predominance in CT: particularly of the thoracopagus type and a significant male predominance in parapagus and parasitic types: significant differences in prevalence by ethnicity and an apparent increasing prevalence trend in South American countries. No genetic, environmental or demographic significant associated factors were identified. Further work in epidemiology and molecular research is necessary to understand the etiology and pathogenesis involved in the development of this fascinating phenomenon of nature.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".