Use of micronucleus assays for the prediction and detection of cervical cancer: a meta-analysis
Bibliographic record
Abstract
Cervical cancer (CC) is the fourth most common cancer in women; the survival rates depend strongly on its early detection. The Pap test is the most frequently used diagnostic tool, but due to its limited sensitivity/specificity, additional screening tests are needed. Therefore, we evaluated the use of micronucleus (MN) assays with cervical cells for the prediction and diagnosis of CC. MN reflects structural and numerical chromosomal aberrations. A search was performed in Pubmed, Scopus, Thomson ISI and Google Scholar. Subsequently, meta-analyses were performed for different grades of abnormal findings in smears and biopsies from patients which were diagnosed with CC. Results of 21 studies in which findings of MN experiments were compared with data from Pap tests show that higher MN frequencies were found in women with abnormal cells that are indicative for increased cancer risks. MN frequency ratios increased in the order inflammation (2.1) < ASC-US and ASC-H (3.3) < LGSIL (4.4) < HGSIL (8.4). Furthermore, results are available from 17 investigations in which MN were scored in smears from patients with neoplasia. MN rates increased with the degree of neoplasia [CIN 1 (4.6) < CIN 2 (6.5) and CIN 3 (10.8)] and were significantly higher (8.8) in CC patients. Our meta-analysis indicates that the MN assay, which is easy to perform in combination with Pap tests, may be useful for the detection/prediction of CC. However, standardization (including definition of the optimal cell numbers and stains) and further validation is necessary before the MN test can be implemented in routine screening.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".