Bibliographic record
Abstract
A functional in vitro mode ro ch Breast carcinoma is orchestrated by a complex series of in the stroma’s influence on tumor growth [5,6]. Many Hosein et al. BMC Cancer (2015) 15:130 DOI 10.1186/s12885-015-1117-0human fibroblasts demonstrated that human fibroblasts3Department of Oncology, McGill University, Montreal, Canada Full list of author information is available at the end of the articlemolecular events and biological processes involving the contributions of several cell types [1,2]. Despite the fact that most of our understanding of cancer centers on those events taking place within the cancer epithelium, the cancer-associated stroma also plays a co-dominant role in shaping the biological and clinical fates of the disease [3,4]. Specifically, the carcinoma-associated reports have focused on the role that CAFs have in regulating TGF-β signalling and angiogenesis through secreted factors such as SDF-1 and VEGF [7-9]. CAFs have shown the ability to both promote [10] and repress [11] MCF-7 cell growth in vitro, in addition to having no effect at all [12]. Importantly, none of these studies took account of possible inter-patient CAF het-erogeneity largely because, unlike tumor heterogeneity, little data exists about inter-patient CAF heterogeneity. In particular, one in vitro model using a series of breast cancer cell lines directly co-cultured with normal
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.497 | 0.382 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".