World Health Organization Guidelines for treatment of cervical intraepithelial neoplasia 2-3 and screen-and-treat strategies to prevent cervical cancer
Bibliographic record
Abstract
BACKGROUND: It is estimated that 1%-2% of women develop cervical intraepithelial neoplasia grade 2-3 (CIN 2-3) annually worldwide. The prevalence among women living with HIV is higher, at 10%. If left untreated, CIN 2-3 can progress to cervical cancer. WHO has previously published guidelines for strategies to screen and treat precancerous cervical lesions and for treatment of histologically confirmed CIN 2-3. METHODS: Guidelines were developed using the WHO Handbook for Guideline Development and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach. A multidisciplinary guideline panel was created. Systematic reviews of randomized controlled trials and observational studies were conducted. Evidence tables and Evidence to Recommendations Tables were prepared and presented to the panel. RESULTS: There are nine recommendations for screen-and-treat strategies to prevent cervical cancer, including the HPV test, cytology, and visual inspection with acetic acid. There are seven for treatment of CIN with cryotherapy, loop electrosurgical excision procedure, and cold knife conization. CONCLUSION: Recommendations have been produced on the basis of the best available evidence. However, high-quality evidence was not available. Such evidence is needed, in particular for screen-and-treat strategies that are relevant to low- and middle-income countries.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".