Bibliographic record
Abstract
Abstract Over the past several years it has become accepted that subtypes of ovarian carcinomas (high grade serous, endometriod, clear cell, low grade serous and mucinous carcinoma) are discrete diseases. Therefore, if we are to improve ovarian cancer morbidity and mortality, cancer prevention, early detection and/or screening strategies must be developed to deal with the challenges posed by each of these diseases. Whereas high grade serous carcinoma is relatively common, the other ovarian carcinoma subtypes represent 30% of cases in total and therefore are all rare. Rare cancers are usually marked by a combination of an unusual cells of origin and unusual mutations or other genomics drivers. We have undertaken a series of genomics studies to identify key drivers in rarer ovarian cancers. Some cancers such as granulosa cell tumors of the ovary and sertoli cell mutations have been found which are either pathognomonic or highly specific for that cancer type. Those mutations have great promise as diagnostics. Since the target cancers are rare such work is not possible without broad ranging international collaborations. For more common entities such as clear cell carcinoma concentrations of mutations can be found such as mutations in ARID1a. As such mutations are nonspecific they are less likely to be used diagnostically however this increases their potential therapeutic value. However, it is likely that the mutations or mutated genes have a specific role in the cell of origin of the cancers involved and therefore cell context specific cell lines of model systems will be required for functional validation. Although rare cancers are by definition unusual and therefore represent smaller medical problems they are of course highly significant to the patients involved and provide a model through which the stratification of management for common cancers can be undertaken. Citation Format: David G. Huntsman. Discovering drivers in rare ovarian cancer subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr IA8.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".