World Health Organization Guidelines: Use of cryotherapy for cervical intraepithelial neoplasia
Bibliographic record
Abstract
BACKGROUND: In 2008, cervical cancer was responsible for 275000 deaths, of which approximately 88% occurred in low- and middle-income countries. In 2009, the World Health Organization (WHO) committed to updating recommendations for use of cryotherapy for cervical intraepithelial neoplasia (CIN). METHODS AND RESULTS: We followed the WHO Handbook for Guidelines Development to develop present guidelines. An expert panel was established, which included clinicians, researchers, program directors, and methodologists. An independent group conducted systematic reviews and produced evidence summaries following the GRADE approach. GRADE evidence profiles were created for 16 key questions about the effects of cryotherapy in the presence of histologically confirmed CIN compared with no treatment and with loop electrosurgical excision procedure, as well as the use of different cryotherapy techniques. We identified a small number of randomized controlled trials or independently controlled observational studies. Surrogate outcomes were reported when evidence about outcomes critical to decision making were not available. The panel made 14 recommendations and documented factors that determined the strength and direction of the recommendations in decision tables. CONCLUSION: The present document summarizes new evidence-based WHO recommendations about the use of cryotherapy in women with histologically confirmed CIN for low-, middle-, and high-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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".