Perinatal invasive malignant diseases: a review of twenty-five cases in South China
Bibliographic record
Abstract
Malignant neoplastic diseases (MND) are unusual complications during perinatal period and compose a dilemma for both patients and the health practitioners. Little is known about the information in Chinese suffering perinatal MND. Analyzing medical records and questionnaire, information on a series of 25 patients with a diagnosis of perinatal MND was collected from 3 medical centers between 1992 and 2004. Among all the 25 patients, 10 selected termination of the pregnancies and the other 15 continued their pregnancies until labor voluntarily, both groups obtain anti-malignancies therapies during the perinatal period. The two groups were not statistically different for the age of pregnancy, gravid and parity number, interval weeks between symptoms emergence and diagnosis of invasive malignant disease, as well as the occurrence rates of major side effects induced by malignant therapies. No statistical differences in overall survival and disease-free survival between the two groups, including the age of pregnancy, gravid and parity number, obstetric bleeding rates, neonatal distress rates, neonatal weight and puerperal morbidity rates. The patients' neonates all show no serious complications. In conclusion, pregnancy may not affect the course of MND, and termination of pregnancy may not benefit the maternal-fetal conditions, in a macroscopical point of view.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".