Does completion of radical hysterectomy improve oncological outcomes of women with clinical early-stage cervical cancer and intraoperative detection of nodal involvement?: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The management of women with clinical early-stage cervical cancer and lymph node involvement detected intraoperatively is heterogeneous and controversial. This paper presents the protocol of a systematic review and meta-analysis regarding the management of this specific population of patients. This proposed study aims to answer the question: does completion of radical hysterectomy improve the oncological outcomes of women with clinical early-stage cervical cancer and intraoperatively detected nodal involvement? Methods and analysis This protocol is drafted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines, and the proposed study will be conducted in accordance with the standard guidelines of ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ and ‘Meta-analysis of Observational Studies in Epidemiology reporting guideline’. Comprehensive literature searches will be performed in PubMed, Embase, Scopus, and Web of Science. The screening of the eligible studies, the extraction of data of interest, and the quality assessment of the included studies will all be independently performed by different members of our team. The primary outcome of this proposed study will be comparing the risk of recurrence or death from cervical cancer and the risk of all-cause death in patients with two different treatments (completion of radical hysterectomy or abandonment of radical hysterectomy); the secondary outcome of this proposed study will be comparing the risk of the grade 3/4 toxicities associated with the two types of management. Given the clinical heterogeneity among the included studies, data on outcomes will be pooled by random-effects models. Heterogeneity will be evaluated using the I2 statistic. The risk of bias for the included studies will be evaluated using the Newcastle-Ottawa Scale or the Cochrane collaboration’s tool. The grade of evidence will be evaluated by two independent members of our team using the Grading of Recommendations, Assessment, Development and Evaluations approach. Ethics and dissemination Ethical approval is not required because there will no primary data collected. The findings of this proposed study will be published in an international peer-reviewed journal. PROSPERO registration number CRD42021273527.
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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 | 0.062 | 0.119 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".