Evaluating Poor Outcome for Manitoba Women with Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal form of gynecological cancer. EOC patients have a low survival rate primarily due to the late stage at which the disease is diagnosed (typically after metastasis), and the high rate of recurrent disease. While the majority of patients respond to initial chemotherapy [typically with a platinum agent (carboplatin) and a taxane (paclitaxel)], up to 75% of EOC patients will relapse within 18 months with chemotherapy-resistant disease. There is a desperate need to identify markers of resistance and novel pathways that may be targeted for treatment. Experiments have been conducted to gain insight into cell surface markers and/or signalling pathways associated with EOC drug resistance. Altered cell surface expression of several candidate pathways has been identified in a drug-resistant EOC cell line, A2780-cp. To extend these studies, further validation of cell surface protein expression will be conducted using additional EOC cell lines and EOC cells isolated from EOC patient ascites. Several serial samples of EOC cells isolated from patients before and after development of drug resistance are also available for testing and validation. Cell surface marker expression will be correlated with clinical parameters indicating platinum drug resistance. The student will learn to assess clinical data regarding patient response to chemotherapy. Moreover, experiments to alter cell surface marker expression in the drugresistant A2780-cp and drug-sensitive A2780-s cell lines will be done to assess the contribution of candidate markers/signaling pathways to development of drug resistance. The proposed studies will test the hypothesis that candidate cell surface markers can be used to predict formation of clinical chemotherapy resistance.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".