Dublin Pathology 2015. 8th Joint Meeting of the British Division of the International Academy of Pathology and the Pathological Society of Great Britain & Ireland
Bibliographic record
Abstract
The pathologist is playing a pivotal role in the identification of patients with familial gynaecological cancer syndromes even when there is no personal or family history of neoplasia. This is because the tumour types which occur in the various syndromes are generally fairly constant and predictable. Familial cancer syndromes in which neoplasms may occur in the female genital tract include BRCA1/2, Lynch syndrome (hereditary non-polyposis colorectal cancer syndrome), Peutz-Jeghers syndrome, DICER1 syndrome and hereditary leiomyomatosis and renal cell carcinoma syndrome. Given the close association between genotype and phenotype, the pathologist has a key role in raising the possibility of an underlying cancer syndrome and accurate diagnosis is essential to this end. In the uterine corpus, carcinomas associated with Lynch syndrome tend to be endometrioid rather than non-endometrioid in type and a proportion of these neoplasms are difficult to categorise. It is probable that only high grade serous carcinomas (which in most cases arise from the fallopian tube fimbria rather than the ovary) are associated with BRCA1/ BRCA2 germline mutations. Rarely, other ovarian tumours occur in patients with germline BRCA1/ 2 mutation but these may be coincidental or may represent misclassified high grade serous carcinoma. A scenario can be envisaged whereby all patients with ovarian/ tubal high grade serous carcinoma undergo BRCA testing and all patients with ovarian endometrioid or clear cell carcinoma undergo testing for Lynch syndrome. As such, pathologists need to provide accurate diagnosis. The role of WT1 and p53 in distinguishing between high grade serous carcinomas and endometrioid, low grade serous or clear cell carcinoma in problematic cases is stressed, as is the fact that mixed carcinomas in the ovary are very uncommon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".