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
Epilepsy and multiple sclerosis (MS) are the 2 most common neurologic disorders affecting women of childbearing age. As many as 25,000 women with epilepsy (WWE) give birth annually.1 While similar national estimates for birth prevalence in women with MS (WWMS) are not available, with the average age at MS onset at 30 years, around one fifth of women with MS will bear children after onset of the disease.2 This presents an important opportunity for neurologists caring for these women to provide transparent, evidence-based information to both inquiring patients and to referring physicians who may be providing concurrent care. In planning pregnancy, these women will want to know both the impact of a potential pregnancy on the neurologic disease (e.g., seizure frequency, MS exacerbation), as well as the impact of the disease on pregnancy and birth outcomes. For epilepsy, an extensive amount of useful information on both issues was recently published in a series of evidence-based guidelines in Neurology ®.3,4 There is also substantial information on the short-term impact of pregnancy on MS: the exacerbation rate is reduced during the second and third trimester and increased during the first 3 months following delivery, resulting in no net change in relapse rates over the entire pregnancy year (9 months gestation + 3 months postpartum).5 In addition, exclusive breastfeeding has been recently reported to dramatically reduce the postpartum …
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) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it