Glandular Cell Abnormalities on SurePath Preparations: A Retrospective Review with Cytology-Histology Correlations
Bibliographic record
Abstract
OBJECTIVE: Detecting glandular lesions is challenging by all Pap test methodologies. As the availability of data on identifying glandular abnormalities by SurePath is scarce, we investigated the detection rates and the correlation with histology follow-up. STUDY DESIGN: A total of 105,927 cases (SurePath and conventional) were searched for the diagnosis of atypical glandular cells or higher glandular abnormalities (AGC+) with the corresponding histologic diagnosis. The associations between the Pap test methods and diagnostic categories were assessed by χ2 test. RESULTS: Overall, 0.32% of SurePath (159/49,375) and 0.29% of conventional (164/56,552) cases showed AGC+ (p = 0.38). Histology confirmed significant abnormalities in 42 versus 53.5% of the cases, respectively (p = 0.064); 72.7% (SurePath) versus 65.2% (conventional) of these were glandular in nature (p = 0.37). The diagnosis of neoplasia (favored or definitive) showed malignancy on follow-up in 100% of SurePath cases (12/12). In contrast, 82.1% of these conventional cases disclosed premalignant or malignant lesions by histology (p = 0.12). CONCLUSIONS: AGC+ cases showed higher prevalence on SurePath preparations. Conventional cases had more abnormalities on follow-up, while glandular lesions represented a higher proportion of abnormal histologies following SurePath AGC+s. The positive predictive value of favored or definite neoplasia was higher in SurePath cases. Overall, these differences were not statistically significant.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".