Original Article - Why is high grade squamous intraepithelialneoplasia under-diagnosed on cytology in a quarter of cases? Analysis of smearcharacteristics in discrepant cases
Bibliographic record
Abstract
BACKGROUND: The accuracy of cervical cytology has been quwstioned due to high false negative rate. In order to improve the sensitivity of cytology it is prudent to analyze the factors which hamper with the diagnosis of high grade lesions. AIMS: To study the cyto-histologic agreement in High grade squamous intraepithelial lesions (HSIL) of uterine cervix and to analyze the smear characteristics in discrepant cases. SETTINGS AND DESIGN: Cervical smears of 100 histology proven cases of Cervical intraepithelial neoplasia III ( CIN III ) were retrieved and reviewed to study cyto-histologic agreement in the diagnosis of high grade lesions.. The discrepant smears, undercalled on cytology, were further analyzed to determine the reasons for misinterpretations. Statistical analysis was performed to find out any significant factors for discrepancies. RESULTS: Cytology was able to correctly identify 74 HSILs while in 26 cases a diagnosis of Low grade squamous intraepithelial lesions (LSIL) or below was given. On review, 16 of these non correlating cases could be reclassified as HSIL on cytology while in 10 the diagnosis of LSIL or less persisted. 12/16 (75%) discrepant cases, reclassified as HSIL represented interpretive errors. Sampling errors (7/10) and air drying (5/10) were more frequent in under diagnosed cases. The statistical analysis did not yield any significant differences in the two review groups. CONCLUSION: 26% of HSIL cases were underdiagnosed on cervical smears. The major confounding factors responsible for under interpretation on cytology included air drying artifacts and metaplastic maturation of abnormal cells.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".