Colposcopy in pregnancy
Bibliographic record
Abstract
Colposcopy is a method of enhanced diagnostics of cervical dysplasia, aimed at preventing cervical cancer. Its role is the same in and out of pregnancy. It is often performed after obtaining abnormal results of cervical cytology or a test for human papillomavirus (HPV16 and HPV18), or any other abnormal findings such as polyps, or unexplained bleeding from the cervix. However, in pregnancy, the first screening test is still a cytological examination of the cervical smear. Colposcopy allows for the identification of suspicious lesions, which allows for a biopsy to be collected for histopathological evaluation. Biopsy is not recommended during pregnancy except for the detection of lesions suspected of being invasive. Colposcopy is minimally invasive, generally well tolerated and crucial for the early detection of cervical intraepithelial neoplasia (CIN) and other gynecological lesions. In pregnancy, this role is reduced to observing the cervix for the progression of lesions. Pregnancy-related lesions in the cervix sometimes make it difficult to assess the cervix effectively and reliably. Therefore, the experience of the physician performing colposcopy during pregnancy is of great importance. In this review we want to summarize the current data about colposcopy during pregnancy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".