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
Despite huge advances in obstetric management and technology in recent decades, there has not been an accompanying decrease in patients’ perception of risk during pregnancy. The aim of this paper is to examine the context of risk perception in pregnancy and what practitioners can do to manage it. The modern pregnancy may induce a heightened perception of risk due to increased prenatal testing and surveillance, medico-legal complexity, fertility treatment, and the increasing use of the internet and social media as a source of information. The consequences of an inflated perception of risk during pregnancy include stress, anxiety, and depression, and these issues may have long-lasting implications for patients, their babies, and their families. There are numerous resilience and vulnerability factors that can help care providers identify those who may be predisposed to increased risk perception in pregnancy, and there is a role for both obstetric care providers and psychologists engaged in obstetric settings to manage and reduce risk perception in patients where possible. Ultimately, the medical management of risk during pregnancy can be complex but a thorough understanding of the social and emotional context can assist providers to support their patients through both high- and low-risk pregnancy and birth.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.002 |
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