Diagnostic Performance of Cytocolposcopy versus Biopsy for Premalignant and Malignant Lesions in a Women's Hospital Dysplasia Clinic
Bibliographic record
Abstract
Background: Cervical cancer (CRC) is a public health problem because it is the fourth most common gynecologic neoplasm worldwide. The screening tests used to diagnose this pathology are cervical cytology, which in suspected malignancy or with malignancy requires colposcopy to identify the affected area and thus guide the biopsy, which is the gold standard for diagnosis. Therefore, these tests are complementary, and a high diagnostic concordance is required to make a confident diagnosis. Subjects and Methods: A retrospective, cross-sectional, observational, and analytical study was performed. A total of 1470 medical records were analyzed, of which 175 patients met the inclusion criteria. The cyto-colposcopic diagnostic yield was compared with the histopathologic yield. The concordance between screening tests and the gold standard was calculated using Cohen's kappa coefficient Results: The sample comprised 175 subjects who met the selection criteria (11.9%). The mean age was 34.59 + 11.01 years, ranging from 17 to 65 years. The mean sexual debut was 16.6 years, with a mean of 3.1 ± 2 sexual partners. When patients were classified according to lesion type, the highest percentages were found in low-grade squamous intraepithelial lesions (LSIL). With 45.71, 61.14, and 49.14% for cytologic, colposcopic, and histopathologic examination, respectively. The highest concordance between histopathology and cytology was found in the high-grade squamous intraepithelial lesion (HSIL) with 0.41, and the concordance between histopathology and colposcopy in HSIL and cancer was 0.55 and 0.74, respectively. Conclusions: Papanicolaou tests and colposcopy showed moderate concordance with histopathologic findings; the diagnostic accuracy of colposcopy is superior to that of cytology.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".