Clinical Practice Guidelines on the Screening and Treatment of Precancerous Lesions for Cervical Cancer Prevention in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the third most common gynecological malignancy in Saudi women with an estimated incidence rate of 1.9 cases per 100 000 women-years. More than 40% of cervical cancer cases are diagnosed at advanced stages due to lack of a routine screening program in Saudi Arabia. Thus, national guidelines for routine screening and treatment of precancerous cervical lesions are needed. METHODS: The Saudi Centre for Evidence-Based Healthcare invited a panel of local experts and partnered them with a team from McMaster University in Canada for methodological support, to develop national clinical practice guidelines on the screening and treatment of precancerous lesions for cervical cancer. After the panel identified key clinical questions, the McMaster University working group updated existing systematic reviews that had been used for the 2013 WHO Guidelines for screening and treatment of precancerous lesions for cervical cancer prevention. Recommendations were based on the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach. Those recommendations took into account the available evidence, patient values and preferences, and resource use in the Saudi context. The panel provided recommendations on two major issues: screening for precancerous lesions (cervical intraepithelial neoplasia 2 & 3) and treatment of those lesions to prevent cervical cancer in women who tested positive after screening. CONCLUSIONS: The Saudi expert panel recommends using the HPV DNA test followed by colposcopy or cytology (Pap test) followed by colposcopy to screen for CIN2+ in women at risk of cervical cancer. The panel recommends cryotherapy or loop excision electrosurgery procedure (LEEP) over cold knife cone biopsy to treat women at risk of cervical cancer that tests positive for CIN2+. Universal screening for precancerous cervical dysplasia in women in Saudi Arabia is recommended using HPV testing and or cytology. Either cryotherapy or LEEP are preferred for treatment. LIMITATIONS: National studies on cervical cancer screening modalities and treatment of precancerous cervical lesions, including HPV prevalence and its association with cervical cancer, are scarce.
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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 | 0.028 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".