Protocol for a systematic review and meta-analysis of the impact of cervical cancer prevention initiatives in Ghana
Bibliographic record
Abstract
BACKGROUND: Cervical cancer, though preventable, remains the second most diagnosed cancer and the primary cause of cancer-related deaths among females in Sub-Saharan Africa. The significance of coordinated screening programmes for reducing the burden of cervical cancer in Africa is not well documented. This systematic review will summarize published reports from key databases, grey literature and programme reports to assess the performance of cervical cancer prevention programmes in Ghana. METHODS: To be eligible for inclusion, interventions must target Ghanaian women with cervical cancer screening and prevention strategies using methods such as visual inspection with acetic acid (VIA), mobile colposcopy, HPV DNA testing, cytology (Pap smear), and treatment approaches such as cryotherapy, thermal ablation, loop electrosurgical excision procedure (LEEP). A comprehensive electronic search strategy will be used to identify studies published since database inception, and indexed in MEDLINE, EMBASE, CINAHL and Web of Science. The search strategy will include MeSH terms (and synonyms) relevant to cervical cancer, screening/treatment methods, geographic focus and implementing institution. We will include searches for grey literature, recognizing the value of programmatic and governmental reports that might not appear in traditional databases. Search results will be summarized in line with PRISMA guidelines. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to evaluate and document evidence certainty for all outcomes, internal validity of included reports, inconsistency, indirectness, imprecision, and publication bias. Where sufficient homogeneity exists among included studies in terms of interventions, study designs, populations, and outcome measures, we will perform a meta-analysis to calculate pooled effect estimates and their corresponding 95% confidence intervals. SIGNIFICANCE: This systematic review will assess the performance and impact of cervical cancer screening and prevention programmes conducted in Ghana to date and identify what contextual strategies have delivered the most impact as well as highlight what gaps remain in our understanding of how a nationwide screening programme can be properly construed for maximum impact.
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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 | 0.092 | 0.143 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.026 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.128 | 0.013 |
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".