The psychological impact of a cancer diagnosed during pregnancy: determinants of long‐term distress
Bibliographic record
Abstract
BACKGROUND: Cancer occurs during one in 1000-5000 of the approximately 6 million yearly US pregnancies identified by the American Pregnancy Association. Although a newly diagnosed cancer is associated with substantial distress, little is known about cancer's emotional impact on women when diagnosed during pregnancy, and no studies have been conducted on the subject. OBJECTIVE: The Cancer and Pregnancy Registry was developed by Elyce H. Cardonick MD, specialist in Maternal and Fetal Medicine and Associate Professor of Obstetrics and Gynecology at Robert Wood Johnson Medical School, to examine the consequences of maternal cancer diagnosis and treatment during pregnancy on maternal, fetal, and neonatal outcomes, including the impact of in utero exposure to chemotherapy. METHODS: Participants were asked to complete questionnaires, including measures of psychological distress, permitting the examination of variables associated with long-term psychological distress in women following a cancer diagnosis in pregnancy. RESULTS: Seventy-four women completed the Brief Symptom Inventory-18 and Impact of Event Scale on average 3.8 years (SD 2.5) following their cancer diagnosis. Potential variables related to distress included information on: sociodemographics, disease, pregnancy, birth, cancer treatment, and health status. Multiple regression analyses revealed that women were at higher risk of long-term distress if they had not received fertility assistance, had been advised to terminate the pregnancy, had had a preterm baby, had had a cesarean delivery, had not produced sufficient milk to breastfeed, had been experiencing a recurrence, and/or had undergone surgery post-pregnancy. CONCLUSION: Results are discussed in light of our current knowledge of the normal developmental phase of pregnancy and motherhood.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".