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Record W4412487003 · doi:10.5603/gpl.104421

Colposcopy in pregnancy

2025· article· en· W4412487003 on OpenAlexaff
Edyta Kęczkowska, Natalia Żeber‐Lubecka, Katarzyna Szlendak-Sauer, Monika Nekanda-Trepka, Maciej Brązert, Michał Ciebiera

Bibliographic record

VenueGinekologia Polska · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsColposcopyMedicinePregnancyObstetricsCervixCervical intraepithelial neoplasiaCervical cancerBiopsyDysplasiaGynecologyRadiologyCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.386
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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