Evaluating the Role of Bedrest on the Prevention of Hypertensive Diseases of Pregnancy and Growth Restriction
Bibliographic record
Abstract
BACKGROUND: Evaluating the effect of restricted activity on the development of preeclampsia under experimental clinical settings has been compromised by inherent selection bias and differential misclassification. The aim of our study was to overcome such limitations by using hospitalized bedrest for preterm labor/birth-related indications as an unbiased measure of restricted activity and evaluate its effect on the development of hypertensive diseases of pregnancy. METHODS: We conducted a retrospective cohort study using data from the McGill Obstetrical and Neonatal Database on all pregnancies that took place between 1991 and 2001. We defined "exposure" as hospitalized bed rest for preterm labor/birth related indications and used unconditional logistic regression models to estimate its adjusted effect on the development of hypertensive diseases of pregnancy. RESULTS: Data were available on 36,140 pregnancies. 677 women were hospitalized and prescribed bedrest for either preterm contractions (71%), preterm premature rupture of membranes (18%), an incompetent cervix (8%), or other indications. Among all women, bedrest was associated with a significant reduced risk for developing preeclampsia, 0.27 (0.16-0.48). In a stratified analysis, women delivering prior to 34 weeks of gestation had an even more pronounced reduced risk for developing preeclampsia 0.12 (0.03-0.50) as well as a reduced risk for developing intrauterine growth restriction 0.38 (0.18-0.84). CONCLUSION: When strictly adhered to, bedrest may be an effective measure in the prevention of preeclampsia and early intrauterine growth restriction.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".