Prognostic evaluation and treatment strategies for cervical cancer in pregnancy: a systematic review and meta-analysis
Bibliographic record
Abstract
ABTSTRAC: OBJECTIVE: This study was conducted to evaluate the prognosis of cervical cancer in pregnancy (CCIP) and analyze the clinicopathological factors affecting the prognosis of this cancer. DATA SOURCES: The studies published through July 2024 were systematically retrieved from PubMed, Embase, Web of Science, and Cochrane Library. STUDY ELIGIBILITY CRITERIA: The cohort studies, case-control studies, randomized controlled trials, and non-randomized controlled trials involving CCIP patients with data on 5-year overall survival (OS) were included in this study. STUDY APPRAISAL AND SYNTHESIS METHODS: The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS). A meta-analysis was performed using Stata 15.0, focusing on the 5-year OS and relevant clinicopathological factors. RESULTS: The results demonstrated that the 5-year OS of patients with CCIP was similar to that of non-pregnant patients with cervical cancer (RR = 1.00, 95% CI: 0.94-1.06, P = 0.978). The subgroup analysis results revealed that tumor size (≥ 4 cm), International Federation of Gynecology and Obstetrics (FIGO) stage (≥ IB2), and timing of diagnosis (postpartum) were prognostic factors with statistical significance (P < 0.05). However, such factors as pregnancy termination and timing of delivery did not significantly affect the 5-year OS (P > 0.05). The delivery mode required further validation despite its borderline significance (P = 0.05). CONCLUSION: The results of this study suggest that pregnancy does not exert a significant adverse effect on the long-term survival of patients with cervical cancer. Tumor size (≥ 4 cm), FIGO stage (≥ IB2), and time of diagnosis (postpartum) are identified as unfavorable prognostic factors for CCIP patients, while delivery mode requires further investigation. These findings provide strong evidence to support the optimization of personalized treatment strategies for CCIP patients.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".