Cervical smear adequacy: cellularity references were found to increase both interobserver agreement and unsatisfactory rate
Bibliographic record
Abstract
OBJECTIVES: To determine the degree of interobserver variation in the assessment of conventional cervical smear adequacy as defined by The Bethesda System (TBS) 2001, and to determine the effect of using reference images of known squamous cellularity when performing squamous adequacy assessments. METHODS: Experimental pre-test/post-test design utilizing 70 conventionally prepared cervical smears. Sample smears containing scant squamous cellularity were independently rated on two occasions by six cytotechnologists. Time 1 was without the use of reference images, and Time 2 was aided by cellularity reference images. The kappa statistic was used to compare rater agreement. RESULTS: The level of agreement increased from an average kappa of 0.26 (SD 0.10) for Time 1, to an average kappa of 0.40 (SD 0.15) for Time 2. The difference in mean kappa values at the two assessments was statistically significant (t = 3.71; P = 0.002). Unanimous agreement among the raters was observed for 15 samples (21.42%) at Time 1 (only one of which was classified as unsatisfactory) and 21 samples (30.00%) at Time 2 (12 of which were classified as unsatisfactory). CONCLUSION: Interobserver agreement increased after cellularity reference images were implemented. Using TBS 2001 squamous adequacy criteria and images of known squamous cellularity as references resulted in a decreased number of smears reported as satisfactory.
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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 | 0.020 | 0.064 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".