Prognosis of Gestational Trophoblastic Neoplasia in Women at 40 Years Old and Above: A Multicentre Retrospective Study
Bibliographic record
Abstract
Purpose: To investigate the outcome of different treatment strategies in patients with gestational trophoblastic neoplasia (GTN) in women at 40 years old and above. Patients and Methods: We analysed a historical cohort from 5 referral centres from 5 countries, including all women with GTN treated between 2012 and 2017, who were 40 years old and older. Baseline characteristics and outcome of different treatment strategies were recorded and evaluated. The patients were categorized into low-risk non-metastatic, low-risk metastatic and high-risk, based on the FIGO classification. Results: A total of 141 cases were identified, of which 112 cases fulfilled the inclusion criteria. Mean age was 45.4 years ± 4.2SD. Of 80 patients with LR non-metastatic GTN, 46 women received single agent chemotherapy and 34 a hysterectomy with or without (n = 4) chemotherapy. Higher remission rate and shorter treatment duration (P=0.001) was seen in the group that underwent hysterectomy. Seven of the 14 patients with low-risk, metastatic GTN were cured with methotrexate. Two of the 18 high risk patients died before treatment, four were treated with polychemotherapy; two of them needed second line chemotherapy for incomplete response. Two cases received induction with methotrexate followed by EMA/CO. Ten highrisk patients were treated with hysterectomy and chemotherapy, of these six achieved complete remission, three needed second line chemotherapy, and one patient died during chemotherapy treatment. Conclusion: In this cohort of women with GTN at 40 years old or above, we found high proportions of metastatic and high-risk cases, of methotrexate resistance, and of need for multiple treatment lines. In all groups, hysterectomy was performed, but its role remains controversial in metastatic low-risk and high-risk disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".