Screening for Cervical Cancer: Protocol for Systematic Reviews to Inform Canadian Recommendations
Bibliographic record
Abstract
Abstract Purpose. To inform recommendations by the Canadian Task Force on Preventive Health Care on cervical cancer screening by systematically reviewing evidence of: (a) the effectiveness; (b) test accuracy; (c) individuals’ values and preferences, and (d) strategies aimed at improving screening rates.Methods. De novo reviews will be conducted to evaluate effectiveness and to assess values and preferences. For test accuracy and strategies to improve screening rates, we will integrate studies from existing systematic reviews with search updates to the present. Two Cochrane reviews will provide evidence of adverse pregnancy outcomes from the conservative management of cervical intraepithelial neoplasia. We will search Medline, Embase, and Cochrane Central (except for individuals’ values and preferences, where Medline, Scopus, and EconLit will be searched) via peer-reviewed search strategies, and the reference lists of included studies and reviews. We will search ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform for ongoing trials. Two reviewers will screen potentially eligible studies and agree on those to include. Data will be extracted by one reviewer with verification by another. Two reviewers will independently assess risk of bias and reach consensus. Where possible and suitable, we will pool studies via meta-analysis. We will compare accuracy data per outcome and per comparison using the Rutter and Gatsonis hierarchical summary receiver operating characteristic model and report relative sensitivities and specificities. Findings on values and preferences will be synthesized using a narrative synthesis approach and thematic analysis, depending on study designs. Two reviewers will appraise the certainty of evidence for all outcomes using GRADE (Grading of Recommendations Assessment, Development and Evaluation) and come to consensus.Discussion. The publication of guidance on cervical cancer screening by the Task Force in 2013 focused on cytology. Since 2013, new studies using human papillomavirus tests for detection of cervical cancer have been published that will improve our understanding of screening in primary care settings. This review will inform updated recommendations based on currently available studies and address key evidence gaps noted in our previous review.Systematic review registration: not registered.
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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 | 0.158 | 0.231 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.026 | 0.032 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.093 | 0.018 |
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".