Sensitivity of cervico‐vaginal cytology in endometrial carcinoma: A systematic review and meta‐analysis
Bibliographic record
Abstract
Cervico-vaginal cytology is primarily a cervical cancer screening test. The anatomical continuity of the uterine cavity with the cervix makes the Papanicolaou (Pap) test accessible to evaluate signs of disease shed from the endometrium. Our aim was to determine the sensitivity of routine Pap test in endometrial carcinoma detection and its relationship with clinico-pathologic factors. We performed a systematic review of studies reporting Pap test results prior to diagnosis of or surgery for endometrial carcinoma between 1990 and 2018 in PubMed or Web of Science. Two independent reviewers extracted data and assessed study quality using an adapted Newcastle-Ottawa Quality Assessment Scale and Quality Assessment of Diagnostic Accuracy Studies tool. We identified 45 studies including a total of 6599 women with endometrial cancer. Abnormal Pap test results prior to diagnosis of or surgery for endometrial carcinoma were observed in 45% (95% CI, 40%-50%) of study participants. This percentage was significantly higher among those of non-endometrioid histology compared with endometrioid subtypes (77% [95% CI, 66%-87%] vs 44% [95% CI, 34%-53%], respectively; P heterogeneity <.001). Several clinico-pathologic factors were related to a higher percentage of abnormal Pap test results, including high-stage, myometrial invasion >50%, high histological grade, positive peritoneal cytology, presence of lymph node metastasis, cervical involvement, and lymphovascular invasion (P heterogeneity <.05 for all variables). Routine cervical cytology can detect endometrial cancer in almost half of patients, whereas sensitivity is higher among individuals with non-endometrioid histology or more advanced cancers. This review summarizes the current clinical and prognostic value of cervical cytology in endometrial carcinoma. Recent technological developments using molecular biomarkers may improve accuracy for early cancer detection.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".