Interobserver concordance in the assessment of features used for the diagnosis of cervical atypical squamous cells and squamous intraepithelial lesions (ASC‐US, ASC‐H, LSIL and HSIL)
Bibliographic record
Abstract
OBJECTIVES: Given the well-known poor reproducibility of cervical cytology diagnosis, especially for atypical squamous cells of undetermined significance (ASC-US) and low-grade squamous intraepithelial lesion (LSIL), this study surveyed reproducibility in the assessment of individual cytomorphological features. METHODS: One hundred and fifty cells or groups of cells, with a variety of morphological appearances, including normal cells, high-grade squamous intraepithelial lesion (HSIL), LSIL, ASC-US and ASC cannot exclude HSIL (ASC-H), were precisely marked on 150 different liquid-based cytological preparations. They were analysed by 17 observers who assessed 17 cytological features including nuclear features (chromatin texture, nuclear outline, nuclear shape, etc.), cytoplasmic features (cell shape, cytoplasmic staining, cytoplasmic clearing, etc.) and group characteristics (nuclear polarity, cellular density, etc.). A total of 43,350 data scores were collected in a database using a web-based survey. Kendall's W and relative entropy indexes were utilized to compute concordance indexes of respectively ordinal and nominal variables. RESULTS: Nuclear features have significantly lower reproducibility (0.46) compared with other cytological features (0.59). The feature with least agreement is assessment of chromatin texture. A small but significant difference in concordance was found between two subsets of observers with different levels of experience. CONCLUSION: Most previous studies assessing reproducibility of cytological diagnoses show, at best, moderate reproducibility among observers. This study focused on agreement regarding the presence of constituent morphological features used to recognize dyskaryosis and various grades of squamous intraepithelial lesions. A map of reproducibility indexes is presented that highlights, for daily practice or teaching, the robustness of features used for cytological assessment, recognizing that diagnosis is always based on a combination of features.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".