Real-world utility of Silva pattern in predicting outcomes in cervical adenocarcinoma from a population-based series
Bibliographic record
Abstract
OBJECTIVE: Silva pattern in cervical adenocarcinoma (AC) is associated with lymph node (LN) involvement. Utility of Silva pattern from diagnostic specimen is uncertain. Our aim was to determine concordance and outcomes for prospectively classified Silva pattern. METHODS: This was a single-centre retrospective study of patients with AC and assigned Silva pattern from 2018 to 2024. The primary outcome was concordance between diagnostic and surgical Silva patterns in cases treated surgically, and false negative (FN) rate of diagnostic Silva A where cases were upgraded to Silva B or C on final pathology. Secondary outcomes were LN involvement, stage, and recurrence for each pattern assessed using Fisher's Exact test. RESULTS: Of 107 patients, final Silva A, B, and C patterns were identified in 28 (26.2 %), 34 (31.8 %), and 45 (42.1 %), respectively. Among surgical cases, there was significant discrepancy between diagnostic and surgical Silva pattern (p = 0.004). FN rate of diagnostic Silva A was 17.6 %. LN were positive in 0, 1 (3.3 %), and 7 (15.6 %) with patterns A, B, and C (p = 0.032). There were 7 recurrences, including 3 with Silva B and 4 with Silva C (p = 0.032). Stage was significantly different with 0, 4 (11.8 %) and 17 (37.8 %) of Silva A, B, and C patients presenting with stage IB3-IV disease (p < 0.001). CONCLUSION: Discrepancy exists between diagnostic and surgical Silva patterns. Caution is advised in using diagnostic Silva pattern to guide treatment, particularly deescalating treatment in case of Silva A. Those with Silva C had significantly higher rates of advance stage, nodal involvement, and recurrences compared to Silva A and B.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".