How physicians perceive and utilize information from a teratogen information service: The Motherisk Program
Bibliographic record
Abstract
BACKGROUND: Teratogen information services have been developed around the world to disseminate information regarding the safety of maternal exposures during pregnancy. The Motherisk Program in Toronto, Canada, fields thousands of these inquiries per year. Our primary objective was to evaluate the perception and utilization of information received from us by physicians. Our secondary objective was to examine their information seeking behavior, in particular regarding teratogen information. METHODS: A one page survey was sent to physicians who had called Motherisk for information concerning pregnancy exposures in the previous 30 days for three months. Among the questions that were asked were demographics, which included gender, years in practice, specialty, information resources, and how they utilized the information received from Motherisk. RESULTS: We received 118/200 completed questionnaires (59% response rate). The mean age of the respondents was: 42 +/- 9 years, mean years of practice was: 14 +/- 8 years, males: 46(38%) and females 72(62%) and 95(80%) were family physicians. 56(48%) researched their question prior to calling Motherisk, 106(91%) and passed on the information received to their patient verbatim. The top four resources for information were: 1) The CPS (PDR), 2) textbooks, 3) journals and 4) colleagues. Only 8% used the Medline for gathering information. CONCLUSIONS: Physicians feel that a teratogen information service is an important component in the management of women exposed to drugs, chemicals, radiation and infections diseases etc. during pregnancy. Despite the advent of the electronic age, a minority of the physicians in our survey elected to use electronic means to seek information.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".