Metadata Correction of: Lessons Learned From Methodological Validation Research in E-Epidemiology
Bibliographic record
Abstract
Background: Traditional epidemiological research methods exhibit limitations leading to high logistics, human, and financial burden. The continued development of innovative digital tools has the potential to overcome many of the existing methodological issues. Nonetheless, Web-based studies remain relatively uncommon, partly due to persistent concerns about validity and generalizability. Objective: To summarize findings from methodological studies carried out in the NutriNet-Santé study, a French Web-based cohort study. Methods: On the basis of the previous findings from the NutriNet-Santé e-cohort (>150,000 participants are currently included), we synthesized e-epidemiological knowledge on sample representativeness, advantageous recruitment strategies, and data quality. Results: Overall, the reported findings support the usefulness of Web-based studies in overcoming common methodological deficiencies in epidemiological research, in particular with regard to data quality (eg, the concordance for body mass index [BMI] classification was 93%), reduced social desirability bias, and access to a wide range of participant profiles, including the hard-to-reach subgroups such as young (12.30% [15,118/122,912], <25 years) and old people (6.60% [8112/122,912], ≥65 years), unemployed or homemaker (12.60% [15,487/122,912]), and low educated (38.50% [47,312/122,912]) people. However, some selection bias remained (78.00% (95,871/122,912) of the participants were women, and 61.50% (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred. Conclusions: Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Santé cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies. ClinicalTrial: not applicable
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 | 0.040 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".