Sensitivity and Specificity of Computerized Algorithms to Classify Gestational Periods in the Absence of Information on Date of Conception
Bibliographic record
Abstract
To evaluate the accuracy of computerized algorithms for pinpointing periods of exposure to medications during pregnancy in the absence of data on timing of conception, the authors used data from a population-based sample of nonmalformed infants in the Slone Epidemiology Center Birth Defects Study in 1998-2006 (United States and Canada; N = 3,177). The standard was defined as any antiinfective use from 2 weeks after the last menstrual period through the third gestational month, which was compared with results obtained after defining the beginning of pregnancy as either 270 days before the birth date (delivery-date algorithm) or the date of the first prenatal visit (pregnancy-indicator algorithm). The sensitivity was 92% (95% confidence interval: 88, 95) for the delivery-date algorithm and 59% (95% confidence interval: 53, 65) for the pregnancy-indicator algorithm. The specificity was higher than 98% for both algorithms. The sensitivity for the delivery-date algorithm among women with preterm births was 66% (95% confidence interval: 49, 80). For women without pregnancy complications, subtraction of 270 days from the delivery date might be accurate for timing first-trimester prescription drug use in automated databases. However, the sensitivity of this algorithm is lower for preterm deliveries, suggesting limited validity to assess drug safety for pregnancy outcomes associated with prematurity.
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 | 0.031 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".