The Effects of Dexamethasone on Breast Cancer and Chemotherapeutic Treatment
Bibliographic record
Abstract
Breast cancer is the most common cancer among Canadian women and is the second leading cause of death by cancer in Canadian Women. Triple-negative breast cancer (TNBC) accounts for approximately 15% of all breast cancer diagnoses. Unlike other breast cancer subtypes, it does not express well-defined molecular targets, such as hormone receptors, that allow targeted treatment. TNBCs are therefore treated with cocktails of potent cytotoxic chemical therapies. Chemotherapy treatment often causes adverse side effects such as hypersensitive reactions, nausea, and vomiting, which are combatted with synthetic glucocorticoids, such as Dexamethasone (Dex). It is of clinical significance to understand the impact Dex has on breast cancer behavior and how it influences chemotherapy treatment response. This literature review compiles data that investigates the impact of Dex on breast cancer through experimental design and clinical studies. It was found that administration of Dex promotes cell survival and metastasis in some breast cancer subtypes. It is important to understand the effects of glucocorticoids on breast cancer cell biology and the interaction with chemotherapeutic agents, which can help lead to alternative therapeutic strategies and improved patient outcomes.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".