Tea consumption during pregnancy and the risk of pre‐eclampsia
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of tea consumption during pregnancy on the risk of pre-eclampsia. METHODS: A case-control study was carried out among nulliparous pregnant women in Quebec between January 2003 and March 2006. Data were collected using a structured study questionnaire. A total of 92 women with pre-eclampsia and 245 controls were analyzed. Univariate analysis and multivariate regression were performed to examine the association between tea consumption and pre-eclampsia. RESULTS: Compared with non-tea drinking during pregnancy, the crude odds ratio (OR) and adjusted OR (aOR) of pre-eclampsia for tea drinking were 1.34 (95% CI, 0.80-2.25) and 1.39 (95% CI, 0.81-2.41), respectively. The OR and aOR of severe pre-eclampsia for tea drinking were 1.39 (95% CI, 0.78-2.46) and 2.14 (95% CI, 1.01-4.54), respectively. The aORs for persistent tea consumption in pre-eclampsia and severe pre-eclampsia were 1.88 (95% CI, 1.01-3.51) and 1.95 (95% CI, 1.06-3.57), respectively. CONCLUSION: Persistent tea drinking during pregnancy may be associated with an increased risk of pre-eclampsia.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".