Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: This study reviews what we know about preconception care, its definition, goals, and content; the science behind the recommended interventions; opportunities for implementing preconception care; and the challenges facing its implementation. RECENT FINDINGS: There is solid scientific evidence that many interventions will improve pregnancy outcomes if delivered before pregnancy or early in pregnancy. Experts continue to explore the most effective means for implementing preconception care, taking into consideration issues related to policy, finance, public health practice, research/surveillance, and consumer and provider education. SUMMARY: Over the past 4 years, there has been renewed interest and a great emphasis on preconception health and healthcare as alternative and additional approaches to counter the persistent increasing incidence in adverse pregnancy outcomes in the United States. Following the publication of the 'Recommendations to Improve Preconception Health and Healthcare' in 2006, many state and local health departments initiated programs to implement the recommendations. Several countries such as Canada, Belgium, and the Netherlands have also started to implement preconception care programs. There are many opportunities for promoting preconception health and providing preconception care; however, making preconception care a standard practice continues to face many barriers.
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.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it