Cervical Adenocarcinoma: A Comparison of the Reproducibility of the World Health Organization 2003 and 2014 Classifications
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to compare the reproducibility of malignant glandular tumors of the uterine cervix classified per World Health Organization (WHO) 2003 and 2014. MATERIALS AND METHODS: Two pathologists reviewed 228 cases composed of adenocarcinoma in situ and 22 adenocarcinoma histotypes and selected 405 representative hematoxylin and eosin slides, which were digitally scanned. Six other pathologists (3 gynecological and 3 anatomical) independently reviewed and classified the images per both WHO classifications. One year later, they classified a random sample of 25 cases. Inter- (inter-OR) and intra-observer (intra-OR) reproducibility of the 6 pathologists and separately for gynecological compared with anatomical pathologists was tested using κ statistics. RESULTS: Both classifications were collapsed into 6 categories as benign, adenocarcinoma in situ, and mucinous, endometrioid, rare, and adenosquamous-miscellaneous carcinomas. WHO 2014 had an additional category: endocervical adenocarcinoma, usual type. Inter-observer κ values were more reliable than the intra-OR results based on 95% CIs. The average inter-OR κ values with both classifications were moderate between the 6 pathologists and between the 3 anatomical pathologists. In contrast, they were substantial between the 3 gynecological pathologists. With both classifications, the average intra-OR κ values of the 6 pathologists and both pathologist groups trended toward substantial. CONCLUSIONS: Reproducibility among 6 pathologists is unaffected by changes in the WHO 2014 classification and averages moderate between different and trends toward substantial between the same pathologist. Reproducibility between different pathologists can improve to substantial when they have expertise in gynecological pathology.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".