Effect of lung metastasis on the treatment and prognosis of patients with gestational trophoblastic neoplasia: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Gestational trophoblastic neoplasia (GTN) is a highly invasive tumor, mainly spreading to the lungs. However, lung metastasis in GTN is usually not considered as an adverse prognostic factor. Therefore, the aim of this study was to summarize the results of previous studies and evaluate the effects of lung metastasis on the treatment and prognosis of GTN. MATERIAL AND METHODS: The study was prospectively registered in PROSPERO (CRD42023372371). Electronic databases including PubMed, Embase, the Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang, and China Biomedical Literature Database were used for a systematical search of relevant studies published up to November 21, 2022. The observational studies reporting the clinical outcomes of GTN patients with and without lung metastasis were selected. The incidences of resistance, relapse, and mortality of GTN patients were extracted and successively grouped based on the presence of lung metastasis. The pooled relative risks (RRs) and 95% confidence interval (95% CI) of the eligible studies were calculated. The qualities of included studies were assessed with the Newcastle-Ottawa Scale and the certainty of evidence was graded based on the GRADE. The meta-analysis was performed using Stata 12.0 and GradePro software. RESULTS: Five publications with 3629 GTN patients were included. The meta-analysis revealed that the GTN with lung metastasis was strongly correlated with first-line chemoresistance (pooled RR = 1.40, 95% CI: 1.22 to 1.61, p < 0.001), recurrence (pooled RR = 3.03, 95% CI: 1.21 to 7.62, p = 0.018), and disease-specific death (pooled RR = 22.11, 95% CI: 3.37 to 145.08, p = 0.001). Ethnicity was also an important factor and Caucasian GTN patients with lung metastasis showed a higher risk of recurrence as revealed by the subgroup analysis (pooled RR = 5.10, 95% CI: 2.38 to 10.94, p < 0.001). CONCLUSIONS: GTN patients with lung metastasis exhibited a higher risk of chemoresistance, relapse, and disease-specific death. Patients with lung metastasis among the Caucasian population had a higher risk of recurrence than Asian populations. Therefore, the presence of lung metastases might be considered as a high-risk factor for prognosis of GTN and deserves more attention in the choice of first-line chemotherapy regimens and follow-up.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".