Dataset for the Reporting of Carcinoma of the Cervix: Recommendations From the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
Cervical carcinoma remains one of the most common cancers affecting women worldwide, despite effective screening programs being implemented in many countries for several decades. The International Collaboration on Cancer Reporting (ICCR) dataset for cervical carcinoma was first developed in 2017 with the aim of developing evidence-based standardized, consistent and comprehensive surgical pathology reports for resection specimens. This 4th edition update to the ICCR dataset on cervical cancer was undertaken to incorporate major changes based upon the updated International Federation of Obstetricians and Gynecologists (FIGO) staging for carcinoma of the cervix published in 2018 and the 5th Edition World Health Organization (WHO) Classification of Female Genital Tumors published in 2020 and other significant developments in pathologic aspects of cervical cancer. This updated dataset was developed by a panel of expert gynecological pathologists and an expert gynecological oncologist, with a period of open consultation. The revised dataset includes "core" and "noncore" elements to be reported; these are accompanied by detailed explanatory notes and references providing the rationale for the updates. Standardized reporting using datasets such as this helps facilitate consistency and accuracy, data collection across different sites and comparison of epidemiological and pathologic parameters for quality and research purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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".