Literature on target therapy of malignant melanoma.
Bibliographic record
Abstract
e21506 Background: Malignant melanoma (MM) of mucosal membranes (excluding anus and head and neck) is a rare but aggressive disease with poor outcome. There is little information available on the mechanism of development, risk factors and management of this tumor, mainly due to the low number of cases. Methods: We performed a literature review on MMM (between 1970-2020) with a focus on nonsurgical management and outcome. Results: We identified 9-papers discussing 1500-MM cases. Formerly, for the management of MMM, Dacarbazine, Interferon b, DAV-Feron, Carboplatin, Imatinib, and Paclitaxel were administered. More recently, novel immunotherapeutic and chemotherapeutic medications such as Immune Checkpoint Inhibitors (ICPI) and anti-VEGF agents have been administered in these patients. In 502 patients of metastatic MMM, adding ipilimumab to dacarbazine increased OS from 9.1-to-11.2 months with one year survival increasing from 36.3%-to-47.3 %. For nivolumab & ipilimumab combination therapy in 361 patients with MMM and CMM, MPSF in nivolumab monotherapy was 3-months and 6.2-months (for MMM and CMM, respectively); but in nivo-ipili combination, MPFS was 5.9-months and 11.7-months (for MMM and CMM, respectively). A benefit of bevacizumab addition to platinum-based chemotherapy in patients showing advanced disease on ICPI is unknown; however, combination-therapy may be reasonable if no access to ICPI or autoimmune diseases. Conclusions: MMM patients could benefit from combination of nivolumab to ipilimumab more than monotherapy with either medication alone.[Table: see text]
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.021 |
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".