World Health Organization Guidelines for treatment of cervical intraepithelial neoplasia 2-3 and screen-and-treat strategies to prevent cervical cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it