Stratified Mucin-Producing Intraepithelial Lesion of the Cervix: Subtle Features Not to Be Missed
Bibliographic record
Abstract
OBJECTIVES: Stratified mucin-producing intraepithelial lesion (SMILE) is an uncommon premalignant lesion of the uterine cervix. A detailed examination of preinvasive SMILE cases including a comparison of the cytologic features with usual-type adenocarcinoma in situ (AIS) and human papillomavirus (HPV) genotyping was performed. STUDY DESIGN: Excisions and preceding Papanicolaou (Pap) tests were retrieved from the files of 2 tertiary care centers. Histologic review estimated the lesional SMILE proportion. Pap tests were reviewed and assessed for architectural, cellular and background features. Cobas® HPV test was performed. RESULTS: 13 cases were identified. Mean/median patient age was 35/33 years (range 23-51 years). Concurrent high-grade squamous intraepithelial lesion was found in 10/13 (77%) and AIS in 8/13 (62%) cases. In 6 cases, SMILE was dominant (≥50%) and represented in 5/6 corresponding Pap tests. Cytology interpretations differed more often in the SMILE-dominant group (p < 0.05). SMILE and AIS had overlapping features. Feathering and prominent nucleoli were absent in SMILE. HPV DNA was detected in all 12 cases tested. HPV 18 was most common (7/12). Excisions with positive/suspicious margins were reported in 5/6 SMILE-dominant versus 3/7 nondominant cases. CONCLUSION: SMILE is best considered as an AIS variant for cytologic, etiologic and management purposes. Cytologic features overlap with AIS, but are more subtle and easily missed. HPV testing may play a role in facilitating SMILE 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".