Bibliographic record
Abstract
OBJECTIVE: This review article outlines the issues involved in (1) the cytologic diagnosis of low-grade squamous intra-epithelial lesion (cervical intraepithelial neoplasia [CIN] 1), (2) histologic diagnosis of CIN 1, (3) the advantages and disadvantages of various management strategies for CIN 1 confirmed by biopsy, and (4) the evolving technology that may be useful for predicting the course of the disease. MATERIALS AND METHODS: A MEDLINE search was conducted using the search terms cervical intraepithelial neoplasia, low-grade dysplasia, mild dysplasia, low-risk squamous intraepithelial lesion, mild dyskaryosis, HPV, colposcopy, histology, and cytology. RESULTS.: Using a loop electrosurgical excision procedure or cone biopsy assessment of the cervix as the gold standard, a cytologic assessment of CIN 1 alone results in a high false-positive rate (51.5%) and a false-negative rate (24%) for CIN 3. The appropriate second test after low-grade squamous intraepithelial lesion (CIN 1) cytologic results includes repeat cervical cytologic analysis. Subsequent human papillomavirus testing provides no advantage and increases the cost of care. Immediate referral to colposcopy is costly but minimizes the percent of women lost to follow-up. Using a loop electrosurgical excision procedure or cone biopsy assessment of the cervix as the gold standard, the colposcopically directed biopsy may give a false-positive result (11.7%) or false-negative result (up to 31%) for CIN 3. One contributing issue is the moderate interobserver reliability of histologic analysis (kappa= 0.46). There are advantages and disadvantages to both the immediate and expectant management strategies. The most crucial concern for immediate treatment is overtreatment, and that for expectant management the high rate of patients lost to follow-up. Novel technologies, including MIB-1, p16(INK)4a, and genetic assessments, may be helpful in predicting those CIN 1 lesions destined to progress or to persist. CONCLUSIONS: The cytologic and histologic diagnosis of CIN 1 is fraught with problems related to the subjectivity of the diagnosis. Both management options are also fraught with concerns. Any technique that can better predict disease course would be an advantage to the care of women with this abnormality.
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.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".