Design and Conduct of an <scp>I</scp>nternet‐Based Preconception Cohort Study in <scp>N</scp>orth <scp>A</scp>merica: <scp>P</scp>regnancy <scp>S</scp>tudy <scp>O</scp>nline
Bibliographic record
Abstract
BACKGROUND: We launched the Boston University Pregnancy Study Online (PRESTO) to assess the feasibility of carrying out an Internet-based preconception cohort study in the US and Canada. METHODS: We recruited female participants age 21-45 and their male partners through Internet advertisements, word of mouth, and flyers. Female participants were randomised with 50% probability to receive a subscription to FertilityFriend.com (FF), a web-based programme that collects real-time data on menstrual characteristics. We compared recruitment methods within PRESTO, assessed the cost-efficiency of PRESTO relative to its Danish counterpart (Snart-Gravid), and validated retrospectively reported date of last menstrual period (LMP) against the FF data. RESULTS: After 99 weeks of recruitment (2013-15), 2421 women enrolled; 1384 (57%) invited their male partners to participate, of whom 693 (50%) enrolled. Baseline characteristics were balanced across randomisation groups. Cohort retention was similar among those randomised vs. not randomised to FF (84% vs. 81%). At study enrollment, 56%, 22%, and 22% couples had been trying to conceive for < 3, 3-5, and ≥ 6 months, respectively. The cost per subject enrolled was $146 (2013 US$), which was similar to our companion Danish study and half that of a traditional cohort study. Among FF users who conceived, > 97% reported their LMP on the PRESTO questionnaire within 1 day of the LMP recorded via FF. CONCLUSIONS: Use of the Internet as a method of recruitment and follow-up in a North American preconception cohort study was feasible and cost-effective.
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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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".