Review: higher caffeine intake during pregnancy increases risk of low birth weight
Bibliographic record
Abstract
Commentary on : Chen LW, Wu Y, Neelakantan N, et al. Maternal caffeine intake during pregnancy is associated with risk of low birth weight: a systematic review and dose response meta-analysis. BMC Med 2014;12:174.[OpenUrl][1][CrossRef][2][PubMed][3] Caffeine is a widely consumed psychoactive substance. Although controversy exists concerning some implications of caffeine consumption during pregnancy, questions concerning maternal caffeine and low birth weight have been largely settled. This study adds further weight to an otherwise consistent conclusion. Using meta-analysis involving 13 prospective studies consisting of more than 100 000 participants from Europe, Canada or the USA, Chen and colleagues assessed … [1]: {openurl}?query=rft.jtitle%253DBMC%2BMed%26rft.volume%253D12%26rft.spage%253D174%26rft_id%253Dinfo%253Adoi%252F10.1186%252Fs12916-014-0174-6%26rft_id%253Dinfo%253Apmid%252F25238871%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1186/s12916-014-0174-6&link_type=DOI [3]: /lookup/external-ref?access_num=25238871&link_type=MED&atom=%2Febnurs%2F18%2F4%2F111.atom
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".