Cytology and outcome of LSIL: cannot exclude HSIL compared to ASC‐H
Bibliographic record
Abstract
OBJECTIVE: The cytological features associated with clinical outcome of 'LSIL cannot exclude HSIL (LSIL-H)' in comparison with 'atypical squamous cells cannot exclude HSIL (ASC-H)' are incompletely described. METHODS: LSIL-H and ASC-H Pap tests reported in a regional laboratory during a 13-month period were reviewed by two pathologists. Cytological features suspicious for HSIL were evaluated against a check list of 52 atypical features. All histology over 2 years of follow up for tests reclassified as LSIL-H and ASC-H was retrieved to determine clinical outcome. Atypical cytological features were correlated with outcome. RESULTS: The review yielded 89 LSIL-H and 86 ASC-H. The highest ranked atypical cytological feature in each group was increased nuclear cytoplasmic ratio. Clinical outcome was positive (CIN II/III or AIS) in 44 (49%) LSIL-H and 33 (38%) ASC-H. Round (P = 0.02) and naked nuclei (P = 0.009) were significant correlates of outcome amongst LSIL-H tests, but no feature correlated with outcome in the ASC-H group. CONCLUSIONS: LSIL-H is different to ASC-H because of the 11% higher frequency of a positive outcome and the cytological features associated with outcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".