Results of the implementation of liquid-based cytology-SurePath in the Ontario screening program
Bibliographic record
Abstract
BACKGROUND: The objective of the current study was to evaluate the adequacy and detection rates of SurePath after its implementation in Ontario. METHODS: The detection and adequacy rates of the SurePath liquid-based cytology system (SP-LBC) were calculated for manually reviewed slides of the year 2002. The adequacy and detection rates from this study group were compared with a historical conventional smear (CS) group from the same laboratories during the same period of the previous year. RESULTS: The SP-LBC study group consisted of 352,680 specimens with cytodiagnoses and the CS group included 378,990 specimens. The unsatisfactory rate for SP-LBC (0.24%) was less than that of the CS group (0.58%). The detection rate of atypical squamous cells (ASC+) by the SP-LBC group (4.69%) was greater than that of the CS group (3.81%), as was the detection rate of low-grade squamous intraepithelial lesions (LSIL+; 2.13% vs. 1.50% in the CS group). There was only a trend toward increased detection of high-grade squamous intraepithelial lesions (HSIL+) in the SP-LBC group (0.34%) relative to the CS group (0.31%), because the detection rate for carcinoma by SP-LBC declined. CONCLUSIONS: The implementation of SP-LBC has been followed by better specimen adequacy and detection rates for ASC+, LSIL+, and a trend of increased detection of HSIL+ relative to CS practice. To determine sensitivity rates, a histopathologic database for cervical carcinoma and precancer needs to be established.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".