Bibliographic record
Abstract
Cancer is the second most common cause of death during the reproductive years, complicating approximately 1/1000 pregnancies. The occurrence of cancer during gestation is likely to increase as a result of a woman's tendency to delay childbearing. Improved diagnostic techniques for malignancies increases detection of cancer during pregnancy. Malignant conditions during gestation are believed to be associated with an increase in poor perinatal and fetal outcomes that are often due to maternal treatment. Physicians should weigh the benefits of treatment against the risks of fetal exposure. To date, most reports have focused on morphologic observations made very close to the time of delivery with little data collected on children's long-term neurodevelopment following in utero exposure to malignancy and treatment. Because the brain differentiates throughout pregnancy and in early postnatal life, damage may occur even after first trimester exposure. The possible delayed effects of treatment on a child's neurological, intellectual and behavioural functioning have never been systematically evaluated. The goal of this report was to summarize all related issues into one review to facilitate both practitioners’ and patients’ access to known data on fetal risks and safety. © 2001 Cancer Research Campaign http://www.bjcancer.com
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.542 | 0.395 |
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".