EPV159/#479 Role of pathology consultant in advancement of diagnostic oncology in underserved countries
Bibliographic record
Abstract
Objectives Quality pathology assessment and reporting of gynecological cancers remains a significant challenge worldwide. Since 2019, the International Gynecological Cancer Society has been offering pathology support to underserved countries through monthly multidisciplinary conferences involving local and expert pathologists. We describe the format of this intervention. Methods An expert pathologist joins conferences at 3–5 sites from underserved countries and discusses the clinical management of challenging cases selected by local gynecologic oncologists. Local and expert gynecologic surgical oncologists participate at each meeting, with occasional participation from radiation oncologists. Local pathologists from two sites consistently participate in these conferences; only these two sites submit pathology images and reports for review by an expert pathology consultant, who provides feedback on the accuracy of the diagnosis and the completeness of the pathology report. Other sites provide only a summary of the pathology diagnosis for discussion. All discussed cases are recorded in an Excel spreadsheet and include details on the management recommendations and the diagnostic pathology reports. Results A pathology report remains a major challenge for local pathologists. The details important for tumor staging and management are often scarce or not present. The sites with involved local pathologists are starting to use International Collaboration on Cancer Reporting (ICCR) checklist for completeness of the report. Conclusions Successful collaboration between local pathologists and international consultants is the first step towards improving the quality of pathology at many sites. The involvement of the local pathologists in the multi-disciplinary conferences and the collaboration with expert pathology consultants is crucial for the advancement of diagnostic oncology in underserved countries.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.701 | 0.376 |
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".