ASC/SIL ratio for cytotechnologists: A survey of its utility in clinical practice
Bibliographic record
Abstract
The atypical squamous cell to squamous intraepithelial lesion (ASC/SIL) ratio for cytotechnologists (CTs) may correlate with screening sensitivity in some laboratory settings. Whether this ratio can be applied to other laboratory settings is not known. We conducted a survey of nine cytology laboratories and correlated the ASC/SIL ratio of individual CTs with other laboratory characteristics. The ASC/SIL ratio for individual CTs varied from 0.6 to 4.5 (mean: 1.9, median: 1.5). The ASC/SIL ratio within individual laboratories varied up to 567%; 25/78 (32%) CTs had an ASC/SIL ratio of less than 1.5, though only three of nine laboratories had more than one CT with a ratio this low. Laboratories that used 100% location guided screening (ThinPrep Imaging System) were much less likely to have a CT with a ratio <1.5 (1/20, 5%) than laboratories that never used location guided screening (14/34, 42%; P = 0.004). In addition, the normalized variance of these same laboratories that used location guided screening was significantly lower than those that did not (normalized standard deviation 0.32 vs. 0.55, P = 0.004). The ASC/SIL ratios did not correlate with laboratory volume, individual workload, or type of specimen preparation (conventional vs. liquid based). The ASC/SIL ratio for CTs varies widely between and within laboratories, and may correlate with the use of location guided screening. Very low ASC/SIL ratios are unusual, and CTs with low ratios may warrant further evaluation.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".