Internet-Based Birth-Cohort Studies: Is This the Future for Epidemiology?
Bibliographic record
Abstract
BACKGROUND: International collaborative cohorts the NINFEA and the ELF studies are mother-child cohorts that use the internet for recruitment and follow-up of their members. The cohorts investigated the association of early life exposures and a wide range of non-communicable diseases. OBJECTIVE: The objective is to report the research methodology, with emphasis on the advantages and limitations offered by an Internet-based design. These studies were conducted in Turin, Italy and Wellington, New Zealand. METHODS: The cohorts utilized various online/offline methods to recruit participants. Pregnant women who became aware volunteered, completed an online questionnaire, thus obtaining baseline information. RESULTS: The NINFEA study has recruited 7003 pregnant women, while the ELF study has recruited 2197 women. The cohorts targeted the whole country, utilizing a range of support processes to reduce the attrition rate of the participants. For the NINFEA and ELF cohorts, online participants were predominantly older (35% and 28.9%, respectively), highly educated (55.6% and 84.9%, respectively), and were in their final trimester of pregnancy (48.5% and 53.6%, respectively). CONCLUSIONS: Internet-based cohort epidemiological studies are feasible, however, it is clear that participants are self-selective samples, as is the case for many birth cohorts. Internet-based cohort studies are potentially cost-effective and novel methodology for conducting long-term epidemiology research. However, from our experience, participants tend to be self-selective. In marked time, if the cohorts are to form part of a larger research program they require further use and exploration to address biases and overcome limitations.
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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 | 0.158 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".