Perforating Rheumatoid Nodule Mimicking Malignant Soft-tissue Mass of the Forearm
Bibliographic record
Abstract
Ovarian cancer (OC) is ranked as the eighth most common gynecological malignancy and is the leading cause of gynecological cancer-related deaths in women worldwide. The response to platinum- and taxane-based chemotherapy is very often poor, and targeted-therapeutics are currently being tested in patients with OC. Sorafenib is a non-selective multiple kinase inhibitor with proven antiproliferative effects in thyroid, renal and hepatocellular carcinoma. Sorafenib acts on vascular endothelial growth factor (VEGF) and on platelet-derived growth factor (PDGF) related pathways. It also influences the rat sarcoma proto-oncogene/rat fibrosarcoma protein kinase/mitogen activated protein kinase (RAS/RAF/MAPK) pathway and blocks tumor growth factor beta-1 (TGF-β-1)-mediated epithelial-mesenchymal transition (EMT). Sorafenib also acts at the epigenetic level altering the histone acetylation pattern. There have been phase I, II and III studies investigation sorafenib in OC. We review several trials in which sorafenib has been administered as single-agent or combined with other chemotherapeutics. Unfortunately, the effect of sorafenib was usually modest and complete response was rarely observed. Adverse effects occurred frequently, including rash, diarrhea, edema and weight gain. Sorafenib evidently blocks EMT in vitro. However, in the conducted trials, sorafenib was administered to patients with highly advanced tumors. We posit that blocking EMT may be more effective in early-stage disease. We also presume that sorafenib would work particularly well in the treatment of clear cell OC, since this type of OC has different molecular characteristics from usual OC and is less sensitive to standard chemotherapy. Furthermore, the combination of sorafenib with other multiple-kinase inhibiting agents, e.g. ABT-869, a targeted-agent mainly acting in the VEGF and PDGF pathways, should be investigated in further detail. It is probable that synergistic effects can be achieved.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".